Studies within the realm of cognitive neuroscience often use different categories of images to define their conditions. However, natural images vary in many image features and not every feature is equally important in describing the differences between the categories. Here, we provide a methodological approach to find as many of the image features as possible, using machine learning as a tool, that have predictive value over the category the images belong to. Our aim is to uncover prototypical characteristics of the categories. To facilitate the use of this method, we offer an open-source, MATLAB-based, toolbox that performs such an analysis and aids the user in visualizing the features of relevance.