Spatiograms are histograms augmented with spatial means and covariances to capture a richer description of the target. We present a particle filtering framework for region-based tracking using spatiograms. Unlike mean shift, the framework allows for non-differentiable similarity measures to compare two spatiograms; we present one such similarity measure, a combination of a recent weighting scheme and histogram intersection. Experimental results show improved performance with the new measure as well as the importance of global spatial information for tracking. The performance of spatiograms is compared with color histograms and several texture histogram methods.