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Several works proposed for the automatic genre musical classification are based on various combinations of parameters, exploiting different models. However, the comparison of all previous works remain impossible since they used different target taxonomies, genre definitions and databases. In this paper, the world largest music database (Real World Computing) is used. Also, different measures related to second-order statistics methods are investigated to achieve the genre classification. Various strategies are proposed for training and testing sessions such as matched conditions, mismatched conditions, long training/testing, long training and short testing. For all experiments, the section of file used in testing has never been presented during the training session. The best classifier achieved 97% and 69% performance when matched and mismatched conditions are used, respectively.