An algorithm for pose estimation of vehicles in synthetic aperture radar imagery is presented. The algorithm utilizes robust features and a structured decision process to achieve high precision for pose angle estimation. It is shown that the pose angle could theoretically be recovered from two simple features: the fill ratio and aspect ratio of the segmented target; and this analysis leads to useful nonlinear feature transformations. Four neural networks are used to make estimates conditional on angular region, and then a general neural network makes the best estimate with the feature set including the regional estimates. The algorithm is demonstrated for MSTAR (Moving and Stationary Target Acquisition and Recognition) data. It is relatively robust to scale variations, and significantly more precise than previously published results.