This paper describes an approach for building detection from multispectral aerial images and lidar data by combining the results derived from statistical and neural network classifiers, which offer complementary information, based on Dempster-Shafer Theory of Evidence. Four study areas with different sensors and scene characteristics were used. First, we filtered the lidar point clouds to generate a Digital Terrain Model (DTM), and then the Digital Surface Model (DSM) and the Normalised Digital Surface Model (nDSM) were generated. After that a total of 25 uncorrelated feature attributes have been generated from the aerial images, the lidar intensity image, DSM and nDSM. Then, three different classification algorithms were used to detect buildings from aerial images, lidar data and the generated attributes. The classifiers used include: Self-Organizing Map (SOM); Classification Trees (CTs); and Support Vector Machines (SVMs). The Dempster-Shafer theory of evidence was then applied for combining measures of evidence from the three classifiers. A considerable amount of the misclassified building pixels were recovered by the combination process .