A review of several recently-developed maximum likelihood template-based automatic target recognition (ATR) algorithms for extended targets in synthetic aperture radar (SAR) imagery data is presented. The algorithms are based on 'gradient' peaks, 'ceiling' peaks, edges, corners, shadows, and rectangular-fits. A weight-based Bayesian
maximum likelihood scheme to combine multiple template-based classifiers is presented. The feature weights are derived from prior recognition accuracies, i.e., confidence levels, achieved by individual template-based classifiers. Application of feature-based weights instead of target specific feature-based weights reduces the resulting ATR accuracy by only a small amount. Preliminary results indicate that (1) the ceiling peaks provide the most target-discriminating power, (2) inclusion of more target-discriminating features leads to higher classification accuracy. Dempster-Shaffer rule of combination is suggested as a potential alternative to the implemented Bayesian decision theory approach to resolve conflicting reports from multiple template-based classifiers.