Depth sensors of low-cost acquisition devices (e.g. Microsoft Kinect, Asus Xtion) are coming into widespread use; however, 3D acquired data are generally large, heterogeneous, and complex to analyse and interpret. In this context, our overall goal is the analysis of the action of a subject in a 3D video, e.g. the action of a human or the movement of its subparts. To this end, the action classification is achieved through the analysis of the temporal variation of geometric (e.g. centroid path, volume variation, activated voxels) and kinematic (e.g. speed) properties in consecutive frames. Then, these descriptors and the corresponding histograms are used to search a frame in a 3D video and to compare 3D videos. Our approach is applied to 3D videos represented as triangle meshes or point sets, and eventually to an underlying skeleton or to markers (if available). Our tests on the MIT, Berkley, i3DPost, NTU, and DUTH data sets confirm the usefulness of the proposed approach for the analysis and comparison of 3D videos, as well as for action classification.