Indoor scene classification is usually approached from a computer vision perspective. However, in some fields like robotics, additional constraints must be taken into account. Specifically, in systems with low resources, state-of-the-art techniques (CNNs) cannot be successfully deployed. In this paper, we try to close this gap between theoretical approaches and real world solutions by performing an in-depth study of the factors that influence classifiers performance, that is, size and descriptor quality. To this end, we perform a thorough evaluation of the visual and depth data obtained with an RGB-D sensor to propose techniques to build robust descriptors that can enable real-time indoor scene classification. Those descriptors are obtained by properly selecting and combining visual and depth information sources. (C) 2017 Elsevier Ltd. All rights reserved.