This work introduces a novel data mining scheme, spatial pyramid mining, to discover association rules at multiple resolutions in order to identify frequent spatial configurations of local features that correspond to classes of logos appearing in real world scenes. By indexing representative examples by the mined rules we can efficiently detect a variety of different lettering or design marks associated with a brand. Features in an image are marked by matching rules to representative examples selected via a weighted cosine similarity measure. Logos are localized in an image via density-based clustering of matched features. Precision vs. recall curves are presented for experiments on a dataset of web images of nearly 1,000 images containing seven popular logo types.