In this paper, we propose a method to classify inverse synthetic aperture radar images from different targets. Our approach can provide efficient features for classification by the combined use of a polar mapping procedure and a well-designed classifier. The resulting feature vectors are able to meet the requirements that efficient features should have: invariance with respect to rotation and scale, small dimensionality, as well as highly discriminative information. Typical experimental examples of the proposed method are provided and discussed.