As more and more research effort is drawn into object tracking algorithms, the ability to assess the performance of these algorithms quantitatively has become a fundamental issue in computer vision. Because tracking systems have to operate in widely varying conditions (different weather conditions, background and target characteristics, etc), a large test bed of video sequences is needed in order to obtain a comprehensive evaluation of a tracker across the whole range of its operating conditions. However, it is very unlikely that a dataset of real video sequences representative of the whole range of operating conditions of a tracker together with its ground truth could be obtained, and building a realistic synthetic dataset of such sequences would require costly advanced simulation platforms. In the new evaluation method proposed in this paper, the operational criteria of the tracking system are turned into objective measures and used to generate a synthetic dataset, non-photorealistic, but statistically representative of the whole range of operating conditions. The assessment of an algorithm using our method provides both a quantitative evaluation of the algorithm and the borders of its validity domain. The performance measurement of an algorithm on a synthetic sequence is shown to be consistent with the measurement on a real sequence with the same criteria. The benefit of this approach is twofold: it provides the developer with a way to concentrate on the weaknesses of his algorithm, and helps the system designer to choose the algorithm that best fits the operating constraints.