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This work presents a two-tier approach through sequential application of intensity contours and formant tracks for accurate Arabic phoneme identification. The recognition system developed is based on data sets of 40 speakers for each Arabic phonetic sound. As a first step towards recognition of phonemes, the sound is sampled and then preprocessed to get formant frequencies and intensity contours. In order to automate the intensity and formant based feature extraction, a generalized regression neural network has been implemented, trained and validated on 21 input features.