Laryngeal diseases usually affect vocal quality. An appropriate acoustic analysis of the voice can be used as an auxiliary non-invasive tool for the pre-diagnosis of laryngeal pathologies. It is possible to evaluate the effectiveness of some medical treatments, as well as pre or post-surgical patient evaluation. This work investigates the use of some speech signal features to discriminate pathological voices from healthy voices. To detect vocal disorders caused by Reinke’s edema or vocal fold paralysis, an acoustic analysis is conducted using three entropy measures (Shannon, relative, and Tsallis) and four cepstral coefficients (cepstral, delta cepstral, weighted cepstral, and weighted delta cepstral). The performance of individual classifiers based on each measure is evaluated. Then, the measures are combined considering three rules: average, product, and weighted sum. Classification accuracy is improved when combinations of acoustic features are considered compared to using individual classifiers.