Most of previous papers about the detection of nude or pornographic images start by the application of a skin detector followed by some kind of shape or geometric modeling. In this work, these two steps are avoided by a bag-of-features (BOF) approach, in which images are represented by histograms of sparse visual descriptors. BOF approaches have been applied successfully to object recognition tasks, but most descriptors used in that case are based on gray level information. Our approach is based on an extension to the well-known SIFT descriptor – called Hue-SIFT – aimed at adding color information to the original SIFT. Experimental results show recognition rates which are similar to those achieved by other approaches in literature, without the need for sophisticated skin or shape models.