To address the classification decision problems in synthetic aperture radar (SAR) image target recognition, a new recognition method based on updated classifier is proposed in this paper. The method uses a convolutional neural network and a sparse representation classifier as the basic classifier to classify samples with unknown categories. The decision results of the two methods are fused, and the reliability of the fused decision results is then determined. Subsequently, test samples with reliable categories are added to the original training samples to update the classifier to obtain more reliable recognition results. The experimental results based on the MSTAR data set show that the recognition accuracy of the method is higher than those of the other methods.