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We propose a novel target recognition algorithm for classification of three types of ground vehicles in the moving and stationary target acquisition and recognition public release database. Algorithms that produce classifiers with large margins, such as support vector machines (SVMs), AdaBoost, etc. are receiving more and more attention in the literature. A real application of AdaBoost for synthetic aperture radar automatic target recognition is presented and the result is compared with conventional classifiers. And we also describe how AdaBoost algorithm can be used as a multiclass classification method as well as a feature fusion method. Results are presented to verify that, the performance of the recognition system is improved significantly, and the method presented in this paper is an effective method for SAR images feature fusion and target recognition.