Graffiti are abundant in most urban neighborhoods and are considered a nuisance and an eyesore. Yet, law enforcement agencies have found them to be useful for understanding gang activities, and uncovering the extent of a gang's territory in large metropolitan areas. The current method for matching and retrieving graffiti is based on a manual database search that is not only inaccurate but also time consuming. We present a content-based image retrieval (CBIR) system for automatic matching and retrieval of graffiti images. Our system represents each graffiti image by a bag of SIFT (Scale Invariant Feature Transform) features. The similarity between a query image and a graffiti image in the database is computed based on the number of matched SIFT features between the two images under certain geometric constraints. Experimental results on two graffiti databases with thousands of graffiti images show encouraging results.