diff --git a/CHANGELOG.md b/CHANGELOG.md
index 24ae0a8ee31e..7800caaf33cb 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ## [2.0.5] - 2022-MM-DD
 ### Added
+- Added support for `normalization_resolver` ([#4926](https://github.com/pyg-team/pytorch_geometric/pull/4926))
 - Added notebook tutorial for `torch_geometric.nn.aggr` package to documentation ([#4927](https://github.com/pyg-team/pytorch_geometric/pull/4927))
 - Added support for `follow_batch` for lists or dictionaries of tensors ([#4837](https://github.com/pyg-team/pytorch_geometric/pull/4837))
 - Added `Data.validate()` and `HeteroData.validate()` functionality ([#4885](https://github.com/pyg-team/pytorch_geometric/pull/4885))
diff --git a/test/nn/test_resolver.py b/test/nn/test_resolver.py
index 8cb2e9caff4d..88874079cae4 100644
--- a/test/nn/test_resolver.py
+++ b/test/nn/test_resolver.py
@@ -5,6 +5,7 @@
 from torch_geometric.nn.resolver import (
     activation_resolver,
     aggregation_resolver,
+    normalization_resolver,
 )
 
 
@@ -34,3 +35,21 @@ def test_aggregation_resolver(aggr_tuple):
     aggr_module, aggr_repr = aggr_tuple
     assert isinstance(aggregation_resolver(aggr_module()), aggr_module)
     assert isinstance(aggregation_resolver(aggr_repr), aggr_module)
+
+
+@pytest.mark.parametrize('norm_tuple', [
+    (torch_geometric.nn.norm.BatchNorm, 'batch_norm', (16, )),
+    (torch_geometric.nn.norm.InstanceNorm, 'instance_norm', (16, )),
+    (torch_geometric.nn.norm.LayerNorm, 'layer_norm', (16, )),
+    (torch_geometric.nn.norm.GraphNorm, 'graph_norm', (16, )),
+    (torch_geometric.nn.norm.GraphSizeNorm, 'graphsize_norm', ()),
+    (torch_geometric.nn.norm.PairNorm, 'pair_norm', ()),
+    (torch_geometric.nn.norm.MessageNorm, 'message_norm', ()),
+    (torch_geometric.nn.norm.DiffGroupNorm, 'diffgroup_norm', (16, 4)),
+])
+def test_normalization_resolver(norm_tuple):
+    norm_module, norm_repr, norm_args = norm_tuple
+    assert isinstance(normalization_resolver(norm_module(*norm_args)),
+                      norm_module)
+    assert isinstance(normalization_resolver(norm_repr, *norm_args),
+                      norm_module)
diff --git a/torch_geometric/nn/resolver.py b/torch_geometric/nn/resolver.py
index 8d843da52e85..00a00ed05930 100644
--- a/torch_geometric/nn/resolver.py
+++ b/torch_geometric/nn/resolver.py
@@ -61,6 +61,22 @@ def activation_resolver(query: Union[Any, str] = 'relu', *args, **kwargs):
     return resolver(acts, act_dict, query, base_cls, *args, **kwargs)
 
 
+# Normalization Resolver ######################################################
+
+
+def normalization_resolver(query: Union[Any, str], *args, **kwargs):
+    import torch
+
+    import torch_geometric.nn.norm as norm
+    base_cls = torch.nn.Module
+    norms = [
+        norm for norm in vars(norm).values()
+        if isinstance(norm, type) and issubclass(norm, base_cls)
+    ]
+    norm_dict = {}
+    return resolver(norms, norm_dict, query, base_cls, *args, **kwargs)
+
+
 # Aggregation Resolver ########################################################