Introduction of RGB-D sensors together with the efforts on open-source point-cloud processing tools boosted research in both computer vision and robotics. One of the key areas which have drawn particular attention is object recognition since it is one of the crucial steps for various applications. In this paper, two spatially enhanced local 3D descriptors are proposed for object recognition tasks: Histograms of Spatial Concentric Surflet-Pairs (SPAIR) and Colored SPAIR (CoSPAIR). The proposed descriptors are compared against the state-of-the-art local 3D descriptors that are available in Point Cloud Library (PCL) and their object recognition performances are evaluated on several publicly available datasets. The experiments demonstrate that the proposed CoSPAIR descriptor outperforms the state-of-the-art descriptors in both category-level and instance-level recognition tasks. The performance gains are observed to be up to 9.9 percentage points for category-level recognition and 16.49 percentage points for instance-level recognition over the second-best performing descriptor. (C) 2015 Elsevier B.V. All rights reserved.