The purpose of this paper is to make an automatic classification of speech into seven emotional classes as anger, boredom, disgust, fear, gladness, neutral and sadness. A two-stage classification composed of several sub-classifiers is proposed. A feature set with 68 features has been computed over 286 speech samples from the Berlin database. The sequential forward selection method (SFS) has been used for each classifiers of the two stages in order to decide the feature subsets in each step. The result for the first stage as three-state classification is 87%, and the global result of the seven emotional classes is 78%, where the correct recognition rate of random classification by chance is about 15%