Automatic Target Recognition (ATR) of moving targets has recently received increased interests. High Range Resolution (HRR) radar mode provides a promising approach which relies on processing high-resolution 'range profiles' over multiple look angles. To achieve a robust, reliable and cost effective approach for HRR-ATR, a model-based approach is investigated in this paper. A subset of the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set was used to study robustness and sensitivity issues related to 1D model-based ATR development and performance. The model is built based on the statistic analysis of the training data and the dependence of the HRR signature on the azimuth is considered. The dependence is approximated by a linear regression algorithm to construct the templates of targets, which gives this approach the name of piecewise linear approach (PWL). Compared with the 1D model-based ATR approach developed by the Wright Laboratory, results are presented demonstrating an increase of about 10% in the correct identification probability of known targets when declaration probability Pdec is above 85% while maintaining a low time-cost.