Recent improvements in scanning technologies such as consumer penetration of RGB-D cameras lead obtaining and managing range image databases practical. Hence, the need for describing and indexing such data arises. In this study, we focus on similarity indexing of range data among a database of range objects (range-to-range retrieval) by employing only single view depth information. We utilize feature based approaches both on local and global scales. However, the emphasis is on the local descriptors with their global representations. A comparative study with extensive experimental results is presented. In addition, we introduce a publicly available range object database which is large and has a high diversity that is suitable for similarity retrieval applications. The simulation results indicate competitive performance between local and global methods. While better complexity trade-off can be achieved with the global techniques, local methods perform better in distinguishing different parts of incomplete depth data. (C) 2015 Elsevier B.V. All rights reserved.