In this paper we address the problem of tracking the motion of articulated objects from their 2-D silhouettes gathered with several cameras. The vast majority of existing approaches relies on a single camera or on stereo. We describe a new method which needs two or more cameras. The method relies on (i) building 3-D observations (points and normals) from image silhouettes and on (ii) fitting an articulated object model to these observations by minimizing their discrepancies. The objective function that gathers these discrepancies takes into account both the scaled algebraic distance from data points to the model surface and the offset in orientation between observed normals and model normals. The combination of a feed-forward reconstruction technique with a robust model-tracking method results in a reliable and efficient method for articulated motion capture.