An automatic target recognition algorithm for synthetic aperture radar (SAR) imagery data is developed. The algorithm classifies an unknown target as one of the known reference targets based on a maximum likelihood estimation procedure. The algorithm helps assess and optimize the favorable effects of multiple image features on recognition accuracy. This study addresses four procedures: (1) feature extraction, (2) training set creation, (3) classification of unknown images, and (4) optimization of recognition accuracy. A three-feature probabilistic method based on extracted edges, corners, and peaks is used to classify the targets. Once the three features are extracted from the target image, binary images are created from each. Training sets, which are used to classify an unknown target, are then created using average Hausdorff distance values for each of the known members of the eight target image types (ZSU-23-4, ZIL131, D7, 2S1, SLICY, BDRM2, BTR60, and T62) included in the publicly available MSTAR test data. The average Hausdorff distance values are acquired from unknown target feature images and are compared to each training set. Each comparison provides the likelihood of the unknown target belonging to one of the eight possible known targets. For each target, eight likelihoods (for eight possible unknown targets) are determined based on the Hausdroff distances and the pre-assigned feature weights. The unknown target is then classified into the target type that has the maximum likelihood estimation value.