In this work a novel content-based musical genre classification approach that uses combination of classifiers is proposed. First, musical surface features and beat related features are extracted from different segments of digital music in MP3 format. Three 15-dimensional feature vectors are extracted from three different parts of a music clip and three different classifiers are trained with such feature vectors. At the classification mode, the outputs provided by the individual classifiers are combined using a majority vote rule. Experimental results show that the proposed approach that combines the output of the classifiers achieves higher correct musical genre classification rate than using single feature vectors and single classifiers.