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Voice pathology detection and classification is a special research field in voice and speech processing for its deep social impact [8]. Historically in the development of new tools for pathology detection, different sets of distortion parameters have been defined, on one hand those estimating perturbations of certain acoustic voice features such as the pitch or energy, others estimating the dispersion of the spectral density of voice. Parameters based on the estimation of the residual of the glottal source related with the mucosal wave are of special interest among these last ones, as it may be shown these to be related with the biomechanical behavior of the vocal cords [2][3]. Therefore the amount of available parameters for pathology detection and classification is rather high. Although not all of them may have the same relevance depending on the specific objective to be covered. The present work is aimed to stress the relevance of different parameters for voice pathology detection using pruning techniques based on Principal Component Analysis. Specific experiments are used to stress the relevance of the parameters in differentiating normophonic and pathologic cases. Possible applications of the method to classify among pathologies could be derived from this study.