Many of the existing object classification methods today rely on convolutional neural networks (CNNs), which are very successful in extracting features from the data. However, CNNs cannot sufficiently address the spatial relationship between features and require large amounts of data for training. In this paper, a new architecture is proposed for 3D object classification, which is an extension of the Capsule Networks (CapsNets) to 3D data. Our proposed 3D CapsNet architecture preserves the orientation and spatial relationship of the extracted features, and thus requires less data to train the network. We compare our approach with a ShapeNet inspired model, and show that our method provides performance improvement especially when training data size gets smaller. We also compare and evaluate several different versions of the 3D Capsnet architecture.