There are different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. Support vector machine is a machine learning algorithm with good performance, its parameters have an important influence on the accuracy of classification, and parameters selection is becoming one of the main research areas of machine learning. This dissertation adopts SVM to recognize the characters of the license plate. Then a number plate recognition algorithm is proposed for character segmentation and recognition. This algorithm employs an SVM to recognize numbers. The algorithm starts with a collection of samples of numbers from number plates. Each character is recognized by an SVM, which is trained by some known samples in advance. In order to recognize a number plate correctly, all numbers are tested one by one using the trained model. Our experimental results depict that our proposed technique has a great extent of recognition accuracy and excessive processing speed as compared to traditional SVM which depended upon the multi-class classifier. This new technique gives a superior direction for automatic number plate recognition. Experiments show that the proposed algorithm has higher recognition accuracy than others, the character recognition accuracy of the training set is greater than 99.95%, and character recognition accuracy of test set reaches 99 %. Therefore we can conclude SVM is a better technique than any other supervised learning.