diff --git a/python/tvm/relay/frontend/pytorch.py b/python/tvm/relay/frontend/pytorch.py
index a1cabcd5ae22..235cec0f096d 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -2056,6 +2056,7 @@ def _get_convert_map(prelude):
         "aten::len"                             : _list_len(prelude),
         "aten::type_as"                         : _type_as(),
         "aten::gather"                          : _gather(),
+        "aten::index_select"                    : _select(),
     }
     return convert_map
 
diff --git a/tests/python/frontend/pytorch/test_forward.py b/tests/python/frontend/pytorch/test_forward.py
index 88203f560641..2302f0fdb74a 100644
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -973,6 +973,7 @@ def forward(self, *args):
     verify_model(View2().float().eval(), input_data=input_data)
     verify_model(View3().float().eval(), input_data=input_data)
 
+
 def test_forward_select():
     torch.set_grad_enabled(False)
     input_shape = [1, 3, 10, 10]
@@ -981,9 +982,26 @@ class Select1(Module):
         def forward(self, *args):
             return args[0].select(1, 1)
 
+    class IndexedSelect(Module):
+        def __init__(self, inp, dim):
+            super().__init__()
+            self.inp = inp
+            self.dim = dim
+            if torch.cuda.is_available():
+                self.inp = self.inp.cuda()
+
+        def forward(self, index):
+            return torch.index_select(self.inp, self.dim, index)
+
     input_data = torch.rand(input_shape).float()
     verify_model(Select1().float().eval(), input_data=input_data)
 
+    x = torch.randn(3, 4)
+    indices = torch.tensor([0, 2])
+    verify_model(IndexedSelect(x, 0).eval(), input_data=indices)
+    verify_model(IndexedSelect(x, 1).eval(), input_data=indices)
+
+
 def test_forward_clone():
     torch.set_grad_enabled(False)
     input_shape = [10]