The paper presents a fast and reliable approach to estimate body postures in outdoor visual surveillance. It works on patches corresponding to people, recognized by two subsystems (motion detection and object recognition) on image sequences coming from a still camera. The proposed algorithm is based on an unsupervised clustering approach and is substantially independent from a-priori assumption about the possible output postures. Horizontal and vertical histograms of the binary shapes associated to humans are selected as features. The Manhattan distance is used for building clusters and for run-time classification. After experimental tests the BCLS (Basic Competitive Learning Scheme) algorithm has been selected for the construction of clusters. The whole approach has been verified on real sequences acquired while typical illegal activities involved in stealing were simulated in an archeological site.