In this paper we investigate the application of Morphological Attribute Profiles to both hyperspectral and LiDAR data to fuse spectral, spatial and elevation data for classification purposes. While hyperspectral data provides a wealth of spectral information, multi-return LiDAR data provides geometrical information on the elevation and the structure of the objects on the ground as well as a measure of their laser cross section. Therefore, hyperspectral and LiDAR data are complementary information sources and potentially their joint analysis can improve classification accuracies. Morphological Profiles (MPs) and Morphological Attribute Profiles (MAPs) have been successfully used as tools to combine spectral and spatial information for classification of remote sensing data. MPs and MAPs can also be used with the LiDAR data to reduce the irregularities in the LiDAR measurements which are inherent with the sampling strategy used in the acquisition process. Experiments carried out on hyperspectral and LiDAR data acquired on a urban area of the city of Trento (Italy) point out the effectiveness of MAPs for the classification process.