In this paper, we introduce a new image database, consisting of examples of artists' work. Successful classification of this database suggests the capacity to automatically recognize an artist's aesthetic style. We utilize the notion of Transform-based Evolvable Features as a means of evolving features on the space, these features then evaluated through a standard classifier. We obtain recognition rates for our six artistic styles - relative to images by the other artists and images randomly downloaded from a search engine - of a mean true positive rate of 0.946 and a mean false positive rate of 0.017. Distance metrics are created from the evolved features, ones designed to indicate the similarity between an arbitrary greyscale image and one of the artistic styles. These distance metrics are capable of ranking control images so that artist-drawn instances appear at the front of the list. Further, we provide some evidence that other images ranked similar by the metric correspond to na\"ive human notions of similarity as well, suggesting the distance metric could serve as a content-based aesthetic recommender.