Abstract: There are few studies on the vehicle recognition methods based on grille regional characteristics both at home and abroad, and its classification efficiency and accuracy is low. Based on the characteristics parameters of structure, shape and texture, the vehicle grille recognition method of the improved C-Support Vector Classification (C-SVC) based on the optimal parameters searching algorithm is proposed in this paper, where the efficiency and the precision are controlled by the dual-angle constraint: on the one hand, based on the Mahalanobis distance and a-principle, and combining with the weighted judgment, the sample data is sorted and used to accelerate the training and testing speed of the Support Vector Machine (SVM) and to improve the algorithm generalization efficiency; on the other hand, in the process of setting kernel function parameter, the optimal parameter iterative searching algorithm based on priori knowledge is designed to improve the classification accuracy of the classifier. The experiment shows that the accuracy rate of vehicle grille recognition method is 97.53%, representing the advantages of higher accuracy and lower false detection rate. It is also proved that this method is able to optimize the classification efficiency and to meet the real-time requirements of recognition.