Material classification is becoming more important in computer vision and digital photography applications, which require accurate classification of objects present in the imaged scene. This is a very challenging task because the sheer diversity of scene content and lighting conditions decreases the usefulness of many color- and texture-based features used in image classification. In this work, we investigate the potential offered by using information outside of the visible spectrum, specifically the near-infrared (NIR). The difference in the NIR images' intensities is not just due to the particular color of the material, but also absorption and reflectance characteristics of the colorant. This relative independency of NIR and color information makes NIR images a prime candidate for classification. The database, on which the training and testing were conducted, consists of textile, tile, linoleum and wood samples. To classify the materials, visible and NIR images were analyzed according to their lightness, texture, and color. The analysis results were the input to a classifier in form of feature vectors. The results show that our database is classified almost exactly. Comparing with visible-only features, wood and textile samples were better classified due to the additional information the NIR images provide.