Knowledge of fuel load and composition is critical in fighting, preventing, and understanding wildfires. Commonly, the generation of fuel maps from remotely sensed imagery has made use of medium-resolution sensors such as Landsat. This paper presents a methodology to generate fuel type maps from high spatial resolution satellite data through object-oriented classification. Fuel maps were derived from QuickBird imagery, which offers a panchromatic and four multispectral bands ranging from 0.61 to 2.44 m resolution. The image used for this paper dated from July 2002 and is located in the NW region of Madrid, Spain. The Prometheus system, a fuel type classification adapted to the ecological characteristics of the European Mediterranean basin, was adopted for this study. Viewed with high-resolution imagery, fuel-related features are often aggregations of pixels exhibiting a variety of spectral properties. Correct identification and classification of these objects requires an explicit consideration of spatial context. We used an object-oriented approach, which allowed context consideration during the classification process, as a complement to traditional pixel-based methods. The map created with this approach was assessed to have greater than 80% accuracy for the prediction of six fuel classes. Results suggested that object-oriented classification of high-resolution imagery has the potential to create accurate and spatially precise fuel maps.