This paper concentrates on an automated analysis of laryngeal images aiming to categorize the images into three decision classes, namely healthy, nodular and diffuse. The problem is treated as an amage analysis and classification task. To obtain a comprehensive description of laryngeal images, multiple feature sets exploiting information on image colour, texture, geometry, image intensity gradient direction, and frequency content are extracted. A separate support vector machine (SVM) is used to categorize features of each type into decision classes. The final image categorization is then obtained which is based on the decisions provided by a committee of support vector machines. Bearing in mind a high similarity of the decision classes, the correct classification rate of over 94 % is obtained while testing the system on 785 laryngeal images that are recorded by the Department of Otolaryngology, Kaunas University of Medicine is rather promising.