The Gaussian process model was applied to synthetic aperture radar (SAR) image target recognition. Gaussian process model was a statistical learning algorithm based on the Bayesian framework, which combines the kernel function and probability judgement to build the classification model. Compared with the traditional classification models, the Gaussian process model could achieve higher classification accuracy and precision. In the implementation of target recognition, the feature vectors from SAR images were used as the inputs while the target labels were employed as the outputs thus training the Gaussian process model. For the test sample to be classified, the posterior probabilities related to different classes were calculated thus determining its target label. In the experiments, typical situations were set up to test the proposed method using the MSTAR dataset. According to the experimental results, the proposed method could achieve 99.28% recognition accuracy for 10 types of targets under standard operating conditions. The average recognition rates at 30° and 45° depression angles were 98.04% and 73.13%, respectively. Under noise corruption, the best performance was achieved by the proposed method at each noise level. The results validated the effectiveness and robustness of the proposed method.