We describe a system for detection and classification of moving targets. The system's change detection and tracking modules are based on background adaptation, with the help of information about targets obtained from preceding time steps. The classification module performs a hybrid classification that combines motion and appearance features. The system is able to perform real-time detection, tracking and classification of targets in outdoor settings. Experiments demonstrate that the proposed hybrid classifier architecture improves classification significantly, thereby permitting real-time discrimination among a considerable number of classes, some of which are quite similar.