Topological localization is a fundamental problem in mobile robotics, since robots must be able to determine their position in order to accomplish tasks. Visual localization and place recognition are challenging due to perceptual ambiguity, sensor noise, and illumination variations. This work addresses topological localization in an office environment using only images acquired with a perspective color camera mounted on a robot platform, without relying on temporal continuity of image sequences. We evaluate state-of-the-art visual descriptors, including Color Histograms, SIFT, ASIFT, RGB-SIFT, and Bag-of-Visual-Words approaches inspired by text retrieval. Our contributions include a systematic, quantitative comparison of these features, distance measures, and classifiers. Performance was analyzed using standard evaluation metrics and visualizations, extending previous experiments. Results demonstrate the advantages of proper configurations of appearance descriptors, similarity measures, and classifiers. The quality of these configurations was further validated in the Robot Vision task of the ImageCLEF evaluation campaign, where the system identified the most likely location of novel image sequences. Future work will explore hierarchical models, ranking methods, and feature combinations to build more robust localization systems, reducing training and runtime while avoiding the curse of dimensionality. Ultimately, this aims toward integrated, real-time localization across varied illumination and longer routes.