In recent years, the increasing use of e-mails has led to the emergence and increase of problems caused by mass unwanted messages which are commonly known as spam. In this study, by using decision trees, support vector machine, Naïve Bayes theorem and voting algorithm, a new version for identifying and classifying spams is provided. In order to verify the proposed method, a set of a mails are chosen to get tested. First three algorithms try to detect spams, and then by using voting method, spams are identified. The advantage of this method is utilizing a combination of three algorithms at the same time: decision tree, support vector machine and Naïve Bayes method. During the evaluation of this method, a data set is analyzed by Weka software. Charts prepared in spam detection indicate improved accuracy compared to the previous methods.