We address the problems of recognizing 10 types of vehicles in imagery formed from synthetic aperture radar (SAR). SAR provides all-weather, day, or night imagery of the battlefield. To aid in the analysis of the copious amounts of imagery available today, automatic target recognition (ATR) algorithms, which are either template-based or model- based, are needed. We enhanced template-based algorithms by using an artificial neural network (ANN) to increase the discriminating characteristics of 10 initial sets of templates. The ANN is a modified learning vector quantization (LVQ) algorithm, previously shown effective with forward-looking IR (FLIR) imagery. For comparison, we ran the experiments with LVQ using three different sized temporal sets. These template sets captured the target signature variations over 60 degrees, 40 degrees, and 20 degrees. We allowed LVQ to modify the templates, as necessary, using the training imager from all 10 targets. The resulting templates represent the 10 target types with greater separability in feature space. Using sequestered test imagery, we compared the pre- and post-LVQ template sets in terms of their ability to discriminate the 10 target types. All training and test imagery is publicly available from the Moving and Stationary Target Acquisition and Recognition program sponsored by the Defense Advanced Research Projects Agency.