We present a method for identifying the authorship of online visual media. Leveraging webcomics as a source of large numbers of images from the same author, we train learning algorithms to classify imagery from ten different webcomics. We propose a set of hand-coded image features, motivated by the various artistic elements, from color palette to line style, that are considered by the author. In so doing, we aim to learn classifications that robustly generalize to new samples of the same comics and which capture similar judgments of style as are made by human viewers. We evaluate the accuracy of our system using various learning algorithms, finding that it successfully classifies novel samples with up to 94% accuracy. To test whether the judgments of our system correspond to human perceptions of composition and style, we use our trained system to classify imagery from untrained webcomics and qualitatively evaluate the similarity of the untrained comics to their returned classifications.