diff --git a/tests/converter_tests/test_converters.py b/tests/converter_tests/test_converters.py
index de9e0a55..c67fe260 100644
--- a/tests/converter_tests/test_converters.py
+++ b/tests/converter_tests/test_converters.py
@@ -203,6 +203,11 @@ def test_batch_norm_nd(nd, with_conv):
     inputs = [torch.randn(*input_size).cuda()]
     cross_validate(module, inputs, fp16_mode=False, tol=1e-1)
 
+    if nd == 1:
+        input_size = [2, 3]
+        inputs = [torch.randn(*input_size).cuda()]
+        cross_validate(module, inputs, fp16_mode=False, tol=1e-1)
+
 
 @pytest.mark.parametrize("dim", [1, -1])
 def test_cat(dim):
diff --git a/torch2trt/converters/native_converters.py b/torch2trt/converters/native_converters.py
index e1dcd145..660fedbd 100644
--- a/torch2trt/converters/native_converters.py
+++ b/torch2trt/converters/native_converters.py
@@ -156,7 +156,7 @@ def convert_batch_norm(ctx):
     bias = bias.detach().cpu().numpy() - running_mean.detach().cpu().numpy() * scale
     power = np.ones_like(scale)
 
-    if ndim == 1:
+    if ndim == 1 or ndim == 0:
         # reshape to 2D
         layer = ctx.network.add_shuffle(input_trt)
         
@@ -171,7 +171,7 @@ def convert_batch_norm(ctx):
 
     layer = ctx.network.add_scale_nd(scale_input, trt.ScaleMode.CHANNEL, bias, scale, power, 1)
 
-    if ndim == 1:
+    if ndim == 1 or ndim == 0:
         # reshape back to 1D
         layer = ctx.network.add_shuffle(layer.get_output(0))
         if len(input.shape) == 2: