diff --git a/chainer/datasets/__init__.py b/chainer/datasets/__init__.py
index b9e2f431f6a1..292f991386a1 100644
--- a/chainer/datasets/__init__.py
+++ b/chainer/datasets/__init__.py
@@ -4,6 +4,7 @@
 from chainer.datasets import mnist  # NOQA
 from chainer.datasets import ptb  # NOQA
 from chainer.datasets import sub_dataset  # NOQA
+from chainer.datasets import svhn  # NOQA
 from chainer.datasets import transform_dataset  # NOQA
 from chainer.datasets import tuple_dataset  # NOQA
 
@@ -25,5 +26,6 @@
 from chainer.datasets.sub_dataset import split_dataset_n_random  # NOQA
 from chainer.datasets.sub_dataset import split_dataset_random  # NOQA
 from chainer.datasets.sub_dataset import SubDataset  # NOQA
+from chainer.datasets.svhn import get_svhn  # NOQA
 from chainer.datasets.transform_dataset import TransformDataset  # NOQA
 from chainer.datasets.tuple_dataset import TupleDataset  # NOQA
diff --git a/chainer/datasets/svhn.py b/chainer/datasets/svhn.py
new file mode 100644
index 000000000000..d27c34e009e8
--- /dev/null
+++ b/chainer/datasets/svhn.py
@@ -0,0 +1,90 @@
+import os
+
+import numpy
+try:
+    from scipy import io
+    _scipy_available = True
+except ImportError:
+    _scipy_available = False
+
+from chainer.dataset import download
+from chainer.datasets import tuple_dataset
+
+
+def get_svhn(withlabel=True, scale=1., dtype=numpy.float32,
+             label_dtype=numpy.int32):
+    """Gets the SVHN dataset.
+
+    `SVHN <http://ufldl.stanford.edu/housenumbers/>` is a dataset
+    similar to MNIST but composed of cropped images of house numbers.
+    The functionality is identical to the MNIST dataset,
+    with the exception that there is no ``ndim`` argument.
+
+    Args:
+        withlabel (bool): If ``True``, it returns datasets with labels. In this
+            case, each example is a tuple of an image and a label. Otherwise,
+            the datasets only contain images.
+        scale (float): Pixel value scale. If it is 1 (default), pixels are
+            scaled to the interval ``[0, 1]``.
+        dtype: Data type of resulting image arrays.
+        label_dtype: Data type of the labels.
+
+    Returns:
+        A tuple of two datasets. If ``withlabel`` is ``True``, both datasets
+        are :class:`~chainer.datasets.TupleDataset` instances. Otherwise, both
+        datasets are arrays of images.
+
+    """
+    if not _scipy_available:
+        raise RuntimeError('scipy is not available')
+
+    train_raw = _retrieve_svhn_training()
+    train = _preprocess_svhn(train_raw, withlabel, scale, dtype,
+                             label_dtype)
+    test_raw = _retrieve_svhn_test()
+    test = _preprocess_svhn(test_raw, withlabel, scale, dtype,
+                            label_dtype)
+    return train, test
+
+
+def _preprocess_svhn(raw, withlabel, scale, image_dtype, label_dtype):
+    images = raw["x"].transpose(3, 2, 0, 1)
+    images = images.astype(image_dtype)
+    images *= scale / 255.
+
+    labels = raw["y"].astype(label_dtype).flatten()
+    # labels go from 1-10, with the digit "0" having label 10.
+    # Set "0" to be label 0 to restore expected ordering
+    labels[labels == 10] = 0
+
+    if withlabel:
+        return tuple_dataset.TupleDataset(images, labels)
+    else:
+        return images
+
+
+def _retrieve_svhn_training():
+    url = "http://ufldl.stanford.edu/housenumbers/train_32x32.mat"
+    return _retrieve_svhn("train.npz", url)
+
+
+def _retrieve_svhn_test():
+    url = "http://ufldl.stanford.edu/housenumbers/test_32x32.mat"
+    return _retrieve_svhn("test.npz", url)
+
+
+def _retrieve_svhn(name, url):
+    root = download.get_dataset_directory('pfnet/chainer/svhn')
+    path = os.path.join(root, name)
+    return download.cache_or_load_file(
+        path, lambda path: _make_npz(path, url), numpy.load)
+
+
+def _make_npz(path, url):
+    _path = download.cached_download(url)
+    raw = io.loadmat(_path)
+    images = raw["X"].astype(numpy.uint8)
+    labels = raw["y"].astype(numpy.uint8)
+
+    numpy.savez_compressed(path, x=images, y=labels)
+    return {'x': images, 'y': labels}