Due to ever more efficient and accurate laser scanning technologies, the analysis of 3D point clouds has become an important task in modern photogrammetry and remote sensing. To exploit the full potential of such data for structural analysis and object detection, reliable geometric features are of crucial importance. Since multiscale approaches have proved very successful for image-based applications, efforts are currently made to apply similar approaches on 3D point clouds. In this paper we analyse common geometric covariance features, pinpointing some severe limitations regarding their performance on varying scales. Instead, we propose a different feature type based on shape distributions known from object recognition. These novel features show a very reliable performance on a wide scale range and their results in classification outnumber covariance features in all tested cases.