We present a system for the robust real-time tracking of human faces. The system utilizes multiple cameras and is built with low-cost standard equipment. A 3D tracking module that uses the information from the multiple cameras is the core of the presented approach. Endowed with a virtual zooming utility, the system provides a close-up view of a face regardless of the persons position and orientation. This best matching front view is found by comparison of color histograms using the Bhattacharyya coefficient. The tracking initialization and learning of the target histograms are done automatically from training data. Results on image sequences of multiple persons demonstrate the versatility of the approach. Telepresence, teleteaching or face recognition systems are examples of possible applications. The system is scalable in terms of the number of computers and cameras, but one computer/laptop with three low-cost FireWire cameras is already sufficient.