A real-world object category can be viewed as a characteristic configuration of its parts, that are themselves simpler, smaller (sub)categories. Recognition of a category can therefore be made easier by detecting its constituent subcategories and combing these detection results. Given a set of training images, each labeled by an object category contained in it, we present an approach to learning: (1) Taxonomy defined by recursive sharing of subcategories by multiple image categories; (2) Subcategory relevance as the degree of evidence a subcategory offers for the presence of its parent; (3) Likelihood that the image contains a subcategory; and (4) Prior that a subcategory occurs. The images are represented as points in a feature space spanned by confidences in the occurrences of the subcategories. The subcategory relevances are estimated as weights, necessary to rescale the corresponding axes of the feature space so that the images with the same label are closer to each other than to those with different labels. When a new image is encountered, the learned taxonomy, relevances, likelihoods, and priors are used by a linear classifier to categorize the image. On the challenging Caltech-256 dataset, the proposed approach significantly outperforms the best categorizations reported. This result is significant in that it not only demonstrates the advantages of exploiting subcategory taxonomy for recognition, but also suggests that a feature space spanned by part properties, instead of direct object properties, allows for linear separation of image classes.