The area of automatic target classification has been a difficult problem for many years. Many approaches involve extracting information from the imagery through a variety of statistical filtering and sampling techniques, resulting in a reduced dimension feature vector that is the input for a learning algorithm. We introduce the support vector machine (SVM) algorithm, which is a wide margin classifier that can provide reasonable results for sparse data sets and whose training speed can be nearly independent of feature vector size. Therefore, we can avoid the feature extraction step and process the images directly. The SVM algorithm has the additional features that there are few parameters to adjust and the solutions are unique for a given training set. We apply SVM to a vehicle classification problem and compare the results to standard neural network approaches. We find that the SVM algorithm gives equivalent or higher correct classification results compared to neural networks.