The aim of this study was to compare acoustic and throat microphones in the voice pathology detection task. Recordings of sustained phonation /a/ were used in the study. Each recording was characterized by a rather large set of diverse features, 1051 features in total. Classification into two classes, namely normal and pathological, was performed using random forest committees. Models trained using data obtained from the throat microphone provided lower classification accuracy. This is probably due to a narrower frequency range of the throat microphone leading to loss of important information. © Kaunas University of Technology, 2014.