The calibration of multiple cameras is an important step in the installation of optical tracking systems. The accuracy of a tracking system is directly related to the quality of the calibration process. Several calibration methods have been proposed in the literature in conjunction with the use of artifacts, called calibration patterns. These patterns, with shape and size known, allow the capture of reference points to compute camera parameters. To yield good results these points must be uniformly distributed over the tracking area. The determination of the reference points in the image is an expensive process prone to errors. The use of a good calibration pattern can reduce these problems. This thesis proposes a new multiple camera calibration method that is efficient and yields better results than previously proposed methods available in the literature. Our method also proposes the use of a new simple calibration pattern based on perspective invariant properties and useful geometric properties. This pattern yields robust reference point identification and more precise tracking. This thesis also revisits the multiple calibration process and suggests a framework to compare the existing methods including the one proposed here. This framework is used to produce a flexible implementation that allows a numerical evaluation that demonstrates the benefits of the proposed method. Finally the thesis presents some conclusions and suggestions for further work.