We describe a particle filtering method for vision based tracking of a hand held calibrated camera in real-time. The ability of the particle filter to deal with non-linearities and non-Gaussian statistics suggests the potential to provide improved robustness over existing approaches, such as those based on the Kalman filter. In our approach, the particle filter provides recursive approximations to the posterior density for the 3-D motion parameters. The measurements are inlier/outlier counts of likely correspondence matches for a set of salient points in the scene. The algorithm is simple to implement and we present results illustrating good tracking performance using a `live' camera. We also demonstrate the potential robustness of the method, including the ability to recover from loss of track and to deal with severe occlusion.