This talk is devoted to the problem of dynamic texture (DT) classification using optic flow features. We provide a principled analysis of local image distortions and their relation to optic flow, then propose a framework for quantitative temporal periodicity analysis of DTs. Adapting the SVD-based algorithm for signal period estimation by Kanjilal et al.(1999), we measure the degree of periodicity of natural dynamic textures. Then we present the results of a comprehensive DT classification study that compares the performances of different flow features for a normal flow algorithm and four different complete flow algorithms. The efficiencies of two flow confidence measures are also studied. Finally, we demonstrate that adding the optic flow based temporal periodicity features improves the classification rates.