We present a system for wide-area multi-object tracking across disjoint camera views. We employ a probabilistic Petri Net-based approach to account for the uncertainties of the vision algorithms (such as unreliable background subtraction, and tracking failure) and to incorporate the available domain knowledge. We combine appearance features of objects as well as the travel-time evidence for target matching and consistent labeling across disjoint camera views. 3D color histogram, Histogram of Oriented Gradients, object size and aspect ratio are used as the appearance features. The distribution of the travel time is modeled by a Gaussian Mixture Model. By incorporating the domain knowledge about the camera configurations and the information about the received packets from other cameras, certain transitions are fired in the probabilistic Petri net. The system is trained to learn different parameters of the matching process. We present wide-area tracking of vehicles as an example where we used three non-overlapping cameras. The first and the third cameras are approximately 150 meters apart from each other with two intersections in the blind region. The results show the success of the proposed method.