Most systems for content based image retrieval (CBIR) employ low level image features as a similarity measure. The problem of CBIR systems is that they are a “black box ” to the user: Queries are specified by sample images, but the features which the CBIR system actually uses are unknown to the user. Hence, unexpected results are difficult to interpret. The problem becomes worse for inexperienced users, who expect the system to understand their query on a symbolic level, while in reality the CBIR system just extracts close-tosignal features. Here we propose to make CBIR systems more “transparent ” by visualization of the employed features. Since non-experts should be able to operate the CBIR system, we argue that features should be visualized as prototypical, artificial images, rather than feature-specific visualizations (such as bar-diagrams for a histogram). We present the visualization of two widely used feature classes, color histograms and texture features, and evaluate in a user study how well the visualizations can be interpreted. 1