Object detection, classification and manipulation are some of the capabilities required by autonomous robots. The main steps in object classification are: segmentation, feature extraction, object representation and learning. To address the problem of learning object classification using multi-view range data, we used a relational approach. The first step of our object classification method is to decompose a scene into shape primitives such as planes, followed by extracting a set of higher-level, relational features from the segmented regions. In this paper, we compare our plane segmentation algorithm with state-of-the-art plane segmentation algorithms which are publicly available. We show that our segmentation outperforms visually and also produces better results for the robot action planning.