diff --git a/hls4ml/converters/pytorch/core.py b/hls4ml/converters/pytorch/core.py
index e45e3c9fae..5a2caba1ea 100644
--- a/hls4ml/converters/pytorch/core.py
+++ b/hls4ml/converters/pytorch/core.py
@@ -109,9 +109,8 @@ def parse_batchnorm_layer(operation, layer_name, input_names, input_shapes, node
             layer['beta_data'] = 0
 
         layer['mean_data'], layer['variance_data'] = get_weights_data(
-            data_reader, layer['name'], ['running_mean', 'running_variance']
+            data_reader, layer['name'], ['running_mean', 'running_var']
         )
-
     in_size = 1
     for dim in input_shapes[0][1:]:
         in_size *= dim
diff --git a/hls4ml/converters/pytorch_to_hls.py b/hls4ml/converters/pytorch_to_hls.py
index ddddbc04c7..961fb735af 100644
--- a/hls4ml/converters/pytorch_to_hls.py
+++ b/hls4ml/converters/pytorch_to_hls.py
@@ -17,30 +17,11 @@ def __init__(self, config):
     def get_weights_data(self, layer_name, var_name):
         data = None
 
-        # Parameter mapping from pytorch to keras
-        torch_paramap = {
-            # Conv
-            'kernel': 'weight',
-            # Batchnorm
-            'gamma': 'weight',
-            # Activiation
-            'alpha': 'weight',
-            'beta': 'bias',
-            'moving_mean': 'running_mean',
-            'moving_variance': 'running_var',
-        }
-
         # Workaround for naming schme in nn.Sequential,
         # have to remove the prefix we previously had to add to make sure the tensors are found
         if 'layer_' in layer_name:
             layer_name = layer_name.split('layer_')[-1]
 
-        if var_name not in list(torch_paramap.keys()) + ['weight', 'bias']:
-            raise Exception('Pytorch parameter not yet supported!')
-
-        elif var_name in list(torch_paramap.keys()):
-            var_name = torch_paramap[var_name]
-
         # if a layer is reused in the model, torch.FX will append a "_n" for the n-th use
         # have to snap that off to find the tensors
         if layer_name.split('_')[-1].isdigit() and len(layer_name.split('_')) > 1:
diff --git a/test/pytest/test_batchnorm_pytorch.py b/test/pytest/test_batchnorm_pytorch.py
new file mode 100644
index 0000000000..a7a0c80247
--- /dev/null
+++ b/test/pytest/test_batchnorm_pytorch.py
@@ -0,0 +1,43 @@
+from pathlib import Path
+
+import numpy as np
+import pytest
+import torch
+from torch import nn
+
+import hls4ml
+
+test_root_path = Path(__file__).parent
+
+in_shape = 16
+atol = 5e-3
+
+
+@pytest.fixture(scope='module')
+def data():
+    np.random.seed(0)
+    X = np.random.rand(100, in_shape)
+    return X
+
+
+@pytest.mark.parametrize('io_type', ['io_parallel', 'io_stream'])
+@pytest.mark.parametrize('backend', ['Vivado', 'Vitis', 'Quartus'])
+def test_batchnorm(data, backend, io_type):
+    model = nn.Sequential(
+        nn.BatchNorm1d(in_shape),
+    ).to()
+    model.eval()
+
+    default_precision = 'ac_fixed<32, 1, true>' if backend == 'Quartus' else 'ac_fixed<32, 1>'
+
+    config = hls4ml.utils.config_from_pytorch_model(model, default_precision=default_precision, granularity='name')
+    output_dir = str(test_root_path / f'hls4mlprj_batchnorm_{backend}_{io_type}')
+    hls_model = hls4ml.converters.convert_from_pytorch_model(
+        model, (None, in_shape), backend=backend, hls_config=config, io_type=io_type, output_dir=output_dir
+    )
+    hls_model.compile()
+
+    # Predict
+    pytorch_prediction = model(torch.Tensor(data)).detach().numpy()
+    hls_prediction = hls_model.predict(data)
+    np.testing.assert_allclose(pytorch_prediction, hls_prediction, rtol=0, atol=atol, verbose=True)