In this paper, an innovative method for tonal and non-tonal language classification using prosodic information is reported. The normalized feature parameters that measure pitch changing speed and pitch changing level are used to train a 3-layer feedforward neural network for the classification. To demonstrate the effectiveness of the proposed method, the recognition rate and the processing time of the novel system are compared with a PPRLM system on a 2-language identification task. For the evaluation results of identifying English/Mandarin, the novel system can achieve a recognition rate of 83.3%, compared with 91.7% of the PPRLM system. However, the processing time of the novel system is only half of that of the PPRLM system. In another extended tonal and non-tonal language classification task with 6 languages, the novel system can achieve a classification rate of 80.6%. Possible applications of the new method to perform pre-classification in language identification are also discussed. Keywords: Tonal language, non-tonal language, pitch changing speed, pitch changing level, language identification.