The article describes the classification of simple movements using a system based on Hidden Markov Models (HMM). Brisk extensions and flexions of the index finger, and movements of the proximal arm (shoulder) and distal arm (finger) were classified using scalp EEG signals. The aim of our study was to develop a system for the classification of movements which show EEG changes at identical scalp electrodes of one hemisphere. The classification of EEG patterns related to movements of one hand is difficult because the disentanglement of movements can only rely on the temporal evolution of EEG changes at one recording site. A large variability-of EEG waveforms requires the use of the context information. The classification procedure was optimized in all parts to increase the recognition score and it was extensively tested on a set of EEG data. The average classification score was 80%, std. deviation 9% for the classification of distal and proximal movements. The classification of extension/flexion reached even better results (due to more accurate localization of the signal source on the scalp). The classification of movement-related EEG data based on HMM yielded higher recognition scores than previously reported classification scores based on artificial neural networks.