This paper addresses the problem of automatically learning common behaviors from long time observations of a scene of interest, with the purpose of classifying actions and, possibly, detecting anomalies. Unsupervised learning is used as an eective way to extract information from the scene with a very limited intervention of the user. The method we propose is rather general, but ts very naturally to a videosurveillance scenario, where the same environment is observed for a long time, usually from a distance. The experimental analysis is based on thousands of dynamic events acquired by three-weeks observations of a single-camera video-surveillance system installed in our department.