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In this paper, we present a wide area surveillance system that detects, tracks and classifies moving objects across multiple cameras. At the single camera level, tracking is performed using a voting based approach that utilizes color and shape cues to establish correspondence. The system uses the single camera tracking results along with the relationship between camera field of view (FOV) boundaries to establish correspondence between views of the same object in multiple cameras. To this end, a novel approach is described to find the relationships between the FOV lines of cameras. The proposed approach combines tracking in cameras with overlapping and/or non-overlapping FOVs in a unified framework, without requiring explicit calibration. The proposed algorithm has been implemented in a real time system. The system uses a client-server architecture and runs at 10 Hz with three cameras.