Nowadays almost everybody spends a lot of time commuting and traveling, so we are all very much interested in smooth use of various roads. Also governing bodies are concerned to assure efficient exploitation of the transportation system. The European Union announced a directive on Intelligent Transport Systems in 2010, to ensure that systems integrating information technology with transport engineering are deployed within the Union. In this paper we address automatic classification of vehicle type, based on audio signals only. Hierarchical classification of vehicles is applied, using decision trees, random forests, artificial neural networks, and support vector machines. A dedicated feature set is proposed, based on spectral ranges best separating the target classes. We show that longer analyzing frames yield better results, and a set of binary classifiers performs better than a single multi-class classifier.