To reduce the influence of aspect angle to synthetic aperture radar (SAR) object recognition and improve recognition rate of SAR distorted object, the algorithm of compressed sensing (CS) and support vector machine (SVM ) decision fusion for SAR object recognition is proposed. SAR object recognition is described as a sparse signal recovery problem in CS based on sparse representation theory, and an object classification result and an aspect angle are obtained through sparse coefficient separately. The classification results are obtained by SVM classifier using rectified and original samples after rectifying the pose of test sample. The final recognition result is obtained through fusion of the three above results based on majority vote. Experimental results demonstrate that, the algorithm of object aspect angle estimation based on compressed sensing result is effective, and the proposed decision fusion algorithm improves deformable object recognition rate significantly as the sample number increases .