Abstract: In this study, we present a new method for voice disorders identification based on a gammachirp wavelet transform and Multilayer Neural Network (MNN). The processing algorithm is based on a hybrid technique which uses the gammachirp wavelets coefficients as input of the MNN. The training step uses a speech database of several pathological and normal voices collected from the national hospital Rabta-Tunis and was conducted in a supervised mode for discrimination of normal and pathology voices and in a second step identification between neural and vocal pathologies (Parkinson, Alzheimer, laryngeal, dyslexia�). Several simulation results will be presented in function of the disease and will be compared with the clinical diagnosis in order to have an objective evaluation of the developed tool.