diff --git a/python/src/lib.rs b/python/src/lib.rs
index 1c139dd..55a72ea 100644
--- a/python/src/lib.rs
+++ b/python/src/lib.rs
@@ -59,7 +59,7 @@ fn result_to_csr(py: Python, x: CsMat<i32>) -> PyResult<PyCsrArray> {
 
 #[pyclass]
 pub struct _HashingVectorizerWrapper {
-    inner: vtext::vectorize::HashingVectorizer,
+    inner: vtext::vectorize::HashingVectorizer<vtext::tokenize::RegexpTokenizer>,
 }
 
 #[pymethods]
@@ -67,7 +67,9 @@ impl _HashingVectorizerWrapper {
     #[new]
     #[args(n_jobs = 1)]
     fn new(obj: &PyRawObject, n_jobs: usize) {
-        let estimator = vtext::vectorize::HashingVectorizer::new().n_jobs(n_jobs);
+        let tokenizer = vtext::tokenize::RegexpTokenizer::new("\\b\\w\\w+\\b".to_string());
+        let estimator = vtext::vectorize::HashingVectorizer::new(tokenizer).n_jobs(n_jobs);
+
         obj.init(_HashingVectorizerWrapper { inner: estimator });
     }
 
@@ -84,7 +86,7 @@ impl _HashingVectorizerWrapper {
 
 #[pyclass]
 pub struct _CountVectorizerWrapper {
-    inner: vtext::vectorize::CountVectorizer,
+    inner: vtext::vectorize::CountVectorizer<vtext::tokenize::RegexpTokenizer>,
 }
 
 #[pymethods]
@@ -92,7 +94,8 @@ impl _CountVectorizerWrapper {
     #[new]
     #[args(n_jobs = 1)]
     fn new(obj: &PyRawObject, n_jobs: usize) {
-        let estimator = vtext::vectorize::CountVectorizer::new().n_jobs(n_jobs);
+        let tokenizer = vtext::tokenize::RegexpTokenizer::new("\\b\\w\\w+\\b".to_string());
+        let estimator = vtext::vectorize::CountVectorizer::new(tokenizer).n_jobs(n_jobs);
         obj.init(_CountVectorizerWrapper { inner: estimator });
     }
 
diff --git a/src/tokenize/mod.rs b/src/tokenize/mod.rs
index a074331..fa570cf 100644
--- a/src/tokenize/mod.rs
+++ b/src/tokenize/mod.rs
@@ -63,6 +63,7 @@ pub trait Tokenizer: fmt::Debug {
 
 /// Regular expression tokenizer
 ///
+#[derive(Clone)]
 pub struct RegexpTokenizer {
     pub pattern: String,
     regexp: Regex,
@@ -98,7 +99,7 @@ impl fmt::Debug for RegexpTokenizer {
 /// ## References
 ///
 /// * [Unicode® Standard Annex #29](http://www.unicode.org/reports/tr29/)
-#[derive(Debug)]
+#[derive(Debug, Clone)]
 pub struct UnicodeSegmentTokenizer {
     pub word_bounds: bool,
 }
@@ -135,7 +136,7 @@ impl Tokenizer for UnicodeSegmentTokenizer {
 /// ## References
 ///
 /// * [Unicode® Standard Annex #29](http://www.unicode.org/reports/tr29/)
-#[derive(Debug)]
+#[derive(Debug, Clone)]
 pub struct VTextTokenizer {
     pub lang: String,
 }
@@ -270,7 +271,7 @@ impl Tokenizer for VTextTokenizer {
 }
 
 /// Character tokenizer
-#[derive(Debug)]
+#[derive(Debug, Clone)]
 pub struct CharacterTokenizer {
     pub window_size: usize,
 }
diff --git a/src/vectorize/mod.rs b/src/vectorize/mod.rs
index 1f751bd..afa57fc 100644
--- a/src/vectorize/mod.rs
+++ b/src/vectorize/mod.rs
@@ -12,19 +12,22 @@ This module allows computing a sparse document term matrix from a text corpus.
 ```rust
 extern crate vtext;
 
+use vtext::tokenize::{VTextTokenizer,Tokenizer};
 use vtext::vectorize::CountVectorizer;
+
 let documents = vec![
     String::from("Some text input"),
     String::from("Another line"),
 ];
 
-let mut vectorizer = CountVectorizer::new();
+let tokenizer = VTextTokenizer::new("en");
+
+let mut vectorizer = CountVectorizer::new(tokenizer);
 let X = vectorizer.fit_transform(&documents);
 // returns a sparse CSR matrix with document-terms counts
 */
 
 use crate::math::CSRArray;
-use crate::tokenize;
 use crate::tokenize::Tokenizer;
 use hashbrown::{HashMap, HashSet};
 use itertools::sorted;
@@ -87,25 +90,25 @@ fn _sum_duplicates(tf: &mut CSRArray, indices_local: &[i32], nnz: &mut usize) {
 }
 
 #[derive(Debug)]
-pub struct CountVectorizer {
+pub struct CountVectorizer<T> {
     lowercase: bool,
-    token_pattern: String,
-    _n_jobs: usize,
+    tokenizer: T,
     // vocabulary uses i32 indices, to avoid memory copies when converting
     // to sparse CSR arrays in Python with scipy.sparse
     pub vocabulary: HashMap<String, i32>,
+    _n_jobs: usize,
 }
 
 pub enum Vectorizer {}
 
-impl CountVectorizer {
+impl<T: Tokenizer + Sync> CountVectorizer<T> {
     /// Initialize a CountVectorizer estimator
-    pub fn new() -> Self {
+    pub fn new(tokenizer: T) -> Self {
         CountVectorizer {
             lowercase: true,
-            token_pattern: String::from(TOKEN_PATTERN_DEFAULT),
             vocabulary: HashMap::with_capacity_and_hasher(1000, Default::default()),
             _n_jobs: 1,
+            tokenizer,
         }
     }
 
@@ -124,14 +127,12 @@ impl CountVectorizer {
     ///
     /// This lists the vocabulary
     pub fn fit(&mut self, X: &[String]) -> () {
-        let tokenizer = tokenize::RegexpTokenizer::new(TOKEN_PATTERN_DEFAULT.to_string());
-
         let tokenize = |X: &[String]| -> HashSet<String> {
             let mut _vocab: HashSet<String> = HashSet::with_capacity(1000);
 
             for doc in X {
                 let doc = doc.to_ascii_lowercase();
-                let tokens = tokenizer.tokenize(&doc);
+                let tokens = self.tokenizer.tokenize(&doc);
 
                 for token in tokens {
                     if !_vocab.contains(token) {
@@ -176,14 +177,12 @@ impl CountVectorizer {
 
         let mut nnz: usize = 0;
 
-        let tokenizer = tokenize::RegexpTokenizer::new(TOKEN_PATTERN_DEFAULT.to_string());
-
         let tokenize_map = |doc: &str| -> Vec<i32> {
             // Closure to tokenize a document and returns hash indices for each token
 
             let mut indices_local: Vec<i32> = Vec::with_capacity(10);
 
-            for token in tokenizer.tokenize(doc) {
+            for token in self.tokenizer.tokenize(doc) {
                 if let Some(_id) = self.vocabulary.get(token) {
                     indices_local.push(*_id)
                 };
@@ -239,14 +238,12 @@ impl CountVectorizer {
         let mut nnz: usize = 0;
         let mut indices_local: Vec<i32> = Vec::new();
 
-        let tokenizer = tokenize::RegexpTokenizer::new(TOKEN_PATTERN_DEFAULT.to_string());
-
         let pipe = X.iter().map(|doc| doc.to_ascii_lowercase());
 
         let mut vocabulary_size: i32 = 0;
 
         for document in pipe {
-            let tokens = tokenizer.tokenize(&document);
+            let tokens = self.tokenizer.tokenize(&document);
 
             indices_local.clear();
 
@@ -277,23 +274,23 @@ impl CountVectorizer {
 }
 
 #[derive(Debug)]
-pub struct HashingVectorizer {
+pub struct HashingVectorizer<T> {
     lowercase: bool,
-    token_pattern: String,
+    tokenizer: T,
     n_features: u64,
     _n_jobs: usize,
     thread_pool: Option<rayon::ThreadPool>,
 }
 
-impl HashingVectorizer {
+impl<T: Tokenizer + Sync> HashingVectorizer<T> {
     /// Create a new HashingVectorizer estimator
-    pub fn new() -> Self {
+    pub fn new(tokenizer: T) -> Self {
         HashingVectorizer {
             lowercase: true,
-            token_pattern: String::from(TOKEN_PATTERN_DEFAULT),
             n_features: 1048576,
             _n_jobs: 1,
             thread_pool: None,
+            tokenizer,
         }
     }
 
@@ -336,14 +333,12 @@ impl HashingVectorizer {
 
         let mut nnz: usize = 0;
 
-        let tokenizer = tokenize::RegexpTokenizer::new(TOKEN_PATTERN_DEFAULT.to_string());
-
         let tokenize_hash = |doc: &str| -> Vec<i32> {
             // Closure to tokenize a document and returns hash indices for each token
 
             let mut indices_local: Vec<i32> = Vec::with_capacity(10);
 
-            for token in tokenizer.tokenize(doc) {
+            for token in self.tokenizer.tokenize(doc) {
                 // set the RNG seeds to get reproducible hashing
                 let hash = seahash::hash_seeded(token.as_bytes(), 1, 1000, 200, 89);
                 let hash = (hash % self.n_features) as i32;
diff --git a/src/vectorize/tests.rs b/src/vectorize/tests.rs
index 390e901..48ec33c 100644
--- a/src/vectorize/tests.rs
+++ b/src/vectorize/tests.rs
@@ -4,14 +4,17 @@
 // <http://apache.org/licenses/LICENSE-2.0>. This file may not be copied,
 // modified, or distributed except according to those terms.
 
+use crate::tokenize::*;
 use crate::vectorize::*;
 
 #[test]
 fn test_count_vectorizer_simple() {
     // Example 1
+    let tokenizer = RegexpTokenizer::new("\\b\\w+\\w\\b".to_string());
 
     let documents = vec!["cat dog cat".to_string()];
-    let mut vect = CountVectorizer::new();
+    let mut vect = CountVectorizer::new(tokenizer.clone());
+
     let X = vect.fit_transform(&documents);
     assert_eq!(X.to_dense(), array![[2, 1]]);
 
@@ -21,8 +24,7 @@ fn test_count_vectorizer_simple() {
         "The sky sky sky is blue".to_string(),
     ];
     let X_ref = array![[0, 1, 0, 1, 1, 2], [1, 0, 1, 0, 3, 1]];
-
-    let mut vect = CountVectorizer::new();
+    let mut vect = CountVectorizer::new(tokenizer);
 
     let X = vect.fit_transform(&documents);
     assert_eq!(X.to_dense().shape(), X_ref.shape());
@@ -38,7 +40,9 @@ fn test_count_vectorizer_simple() {
 fn test_vectorize_empty_countvectorizer() {
     let documents = vec!["some tokens".to_string(), "".to_string()];
 
-    let mut vect = CountVectorizer::new();
+    let tokenizer = RegexpTokenizer::new("\\b\\w+\\w\\b".to_string());
+
+    let mut vect = CountVectorizer::new(tokenizer);
     vect.fit_transform(&documents);
 
     vect.fit(&documents);
@@ -48,8 +52,9 @@ fn test_vectorize_empty_countvectorizer() {
 #[test]
 fn test_vectorize_empty_hashingvectorizer() {
     let documents = vec!["some tokens".to_string(), "".to_string()];
+    let tokenizer = RegexpTokenizer::new("\\b\\w+\\w\\b".to_string());
 
-    let vect = HashingVectorizer::new();
+    let vect = HashingVectorizer::new(tokenizer);
     vect.fit_transform(&documents);
 
     vect.transform(&documents);
@@ -57,12 +62,13 @@ fn test_vectorize_empty_hashingvectorizer() {
 
 #[test]
 fn test_count_vectorizer_fit_transform() {
+    let tokenizer = RegexpTokenizer::new("\\b\\w+\\w\\b".to_string());
     for documents in &[vec!["cat dog cat".to_string()]] {
-        let mut vect = CountVectorizer::new();
+        let mut vect = CountVectorizer::new(tokenizer.clone());
         vect.fit(&documents);
         let X = vect.transform(&documents);
 
-        let mut vect2 = CountVectorizer::new();
+        let mut vect2 = CountVectorizer::new(tokenizer.clone());
         let X2 = vect2.fit_transform(&documents);
         assert_eq!(vect.vocabulary, vect2.vocabulary);
         println!("{:?}", vect.vocabulary);
@@ -87,7 +93,9 @@ fn test_hashing_vectorizer_simple() {
         String::from("The sky is blue"),
     ];
 
-    let vect = HashingVectorizer::new();
+    let tokenizer = VTextTokenizer::new("en");
+
+    let vect = HashingVectorizer::new(tokenizer);
     let vect = vect.fit(&documents);
     let X = vect.transform(&documents);
     assert_eq!(X.indptr(), &[0, 4, 8]);
@@ -116,17 +124,37 @@ fn test_hashing_vectorizer_simple() {
 fn test_empty_dataset() {
     let documents: Vec<String> = vec![];
 
-    let mut vectorizer = CountVectorizer::new();
+    let tokenizer = VTextTokenizer::new("en");
+    let mut vectorizer = CountVectorizer::new(tokenizer.clone());
 
     let X = vectorizer.fit_transform(&documents);
     assert_eq!(X.data(), &[]);
     assert_eq!(X.indices(), &[]);
     assert_eq!(X.indptr(), &[0]);
 
-    let vectorizer = HashingVectorizer::new();
+    let vectorizer = HashingVectorizer::new(tokenizer);
 
     let X = vectorizer.fit_transform(&documents);
     assert_eq!(X.data(), &[]);
     assert_eq!(X.indices(), &[]);
     assert_eq!(X.indptr(), &[0]);
 }
+
+#[test]
+fn test_dispatch_tokenizer() {
+    let tokenizer = VTextTokenizer::new("en");
+    CountVectorizer::new(tokenizer.clone());
+    HashingVectorizer::new(tokenizer);
+
+    let tokenizer = UnicodeSegmentTokenizer::new(false);
+    CountVectorizer::new(tokenizer.clone());
+    HashingVectorizer::new(tokenizer);
+
+    let tokenizer = RegexpTokenizer::new("\\b\\w+\\w\\b".to_string());
+    CountVectorizer::new(tokenizer.clone());
+    HashingVectorizer::new(tokenizer);
+
+    let tokenizer = CharacterTokenizer::new(4);
+    CountVectorizer::new(tokenizer.clone());
+    HashingVectorizer::new(tokenizer);
+}