Conventional speech recognition engines extract Mel Frequency Cepstral Coefficients (MFCC) features from incoming speech. This paper presents a novel approach for feature extraction in which speech is processed according to the Auditory Image Model, a model of human psychoacoustics. We fist describe the proposed front-end, then we present recognition results obtained with the TIMIT database. Comparing with previously published results on the same task, the new approach achieves a 10% improvement in recognition accuracy.