In this paper, new synthetic aperture radar （SAR） image target recognition approach based on multiple views decision fusion is presented. Image chips are represented as feature vectors by 2-D wavelet transformation and principal component analysis algorithm. The feature vectors are classified by using algorithms of support vector machine （SVM）. After multiple views of the same vehicle collected at different aspects classified by SVM, the outputs are then fused using Bayesian approach and the final classification decision is generated. Experiments are implemented with three class targets in Moving and Stationary Target Acquisition and Recognition （MSTAR） Program database. Experimental results indicate that there are significant target recognition performance benefits in the probability of correct classification when three or more views are used for decision fusion. Therefore, the approach proposed is an effective method for SAR image target recognition.