This paper proposes the performance of a new algorithm for vehicles recognition system. This recognition system is based on extracted features on the performance of image's Contourlet transform & achieving standard deviation of Contourlet coefficients matrix in different subbands & various directions. This paper presents the application of three different types of classifiers to the vehicle recognition. They include support vector machine (one versus one), k nearest-neighbor and support vector machine (one versus all). In addition, the proposed recognition system is obtained by using different subbands information as feature vector. So, we could clarify the most important subbands in aspect of having useful information. The performed numerical experiments for vehicles recognition have shown the superiority of Contourlet and standard deviation preprocessing, which are associated with the support vector machine structure (one versus one). The results of this test show, the right recognition rate of vehicle's model in this recognition system, at the time of using total subbands information numbers 3&4 Contourlet coefficients matrix is about 99%. We've gathered a data set that includes 300 images from 5 different classes of vehicles. These 5 classes of vehicles include of: PEUGEOT206, PEUGEOT405, Pride, RENAULT and Peykan. We 've examined 230 pictures as our train data set and 70 pictures as our test data set.