Abstract：Vehicle identification is a very difficult problem and the accuracy of identification results is very low, a method to identify vehicle is proposed based on statistical pattern recognition, adopting nonlinear Support Vector Machine(SVM) to identify the target vehicle. First, the image information of vehicles in the front and the back of the vehicle is collected by using the vehicle-mounted CCD camera. The collected images are filtered by wavelet denoising and processed with image binaryzation in order to eliminate the noise interference. Through the coordinate transformation, one-to-one correspondence relationship between the vehicles in image and the real ones is established. Then, the target vehicle is correctly positioned. Secondly, the processed images are partitioned into 8×8 grids. The ratio of the number of pixels meeting the requirements to the total pixels in each grid is served as the only decision condition for the output (0 or 1) of each grid. The total output for each row could be taken as the characteristic vector's element. The best parameter combination is determined by using the cross validation and genetic algorithm. The vehicles located at10-20m before and after the subject vehicle are taken as the training sample to train the model. The model is verified. Test results show that the algorithm is able to accurately distinguish the types of the vehicles.