Mapping of surface materials in urban areas using aerial imagery is a challenging task. This is because there are numerous materials present in relatively small regions. These materials contribute to the reflected radiance and cannot be mapped effectively using images produced by coarse spectral resolution sensors. Hyperspectral data is produced by sensors with a fine spectral resolution and thus has a significant capability for automatic identification and mapping of urban surface materials. It has a high information content and requires an understanding of the ground material properties and their relation with hyperspectral sensor measurements. In this study an approach for identification of roof surface materials using hyperspectral data is presented. The study is based on an urban area in Ludwigsburg, Germany, using a HyMap data set recorded during the HyMap campaign in August, 2010. The data set has a spatial resolution of 4 m and consists of 125 spectral bands over the wavelength range 0.45 - 2.5 µm. Automatisierte Liegenschaftskarte (ALK) vector data with a layer for buildings is combined with the HyMap data to limit the analysis and reduce confusion between roofs and other features such as roads with similar spectral properties. A spectral library for roofs is compiled based on field and image measurements. In the roof material identification process, supervised classification methods, namely spectral angle mapper (SAM) and spectral information divergence (SID) similarity measures, and the object oriented ECHO (extraction and classification of homogeneous objects) approach are compared using the established spectral library. In addition to the overall shape of spectral curves the position and strength of absorptions features and the brightness are used to enhance material identification. Feature extraction methods such as the discriminant analysis and decision boundary feature extraction are also applied to the data in order to identify features or band combinations suitable for discriminating between the target classes. The identified optimal features from the feature extraction are used to create a new data set which is later classified using the ECHO classifier. The classification results with respect to material types of roofs are presented in this study. The most important results are evaluated using orthophotos, probability maps and field visits.