Synthetic aperture radar (SAR) images inherently present random and complex spatial patterns, which makes the land-cover classification from SAR images a challenging task. A convolutional neural network (CNN) has been applied to the land-cover classification. However, the statistical properties of an SAR image have not yet been explicitly considered by CNN for feature extraction. To address this problem, this letter presents a statistical CNN (SCNN) for land-cover classification from SAR images, which enables the representation of learning and statistical analysis to be implemented with a unified framework. In the proposed SCNN, the distribution of mid-level primitive features, extracted by representation learning, is characterized by their first- and second-order statistics. These statistics are used to fit the land-cover representations, which encode the statistical properties of the SAR image in the feature space. Experiments on the TerraSAR-X data demonstrate that the SCNN is effective and efficient for the land-cover classification from SAR images.