Recent high-resolution satellite images provide an exciting new data source for geospatial information acquisition. This makes it possible to extract man-made objects such as buildings from satellite imagery. The purpose of this paper is to explore a new procedure for building boundary extraction from high resolution satellite imagery such as IKONOS and Digital Building Model (DBM). Spatial registration of DBM data and high resolution satellite images are performed as data pre-processing. It is done in such a way that two data sets are unified in the object coordinate system. Then a classification process is performed for building detection. First the classification is performed without using the DBM, and then we combine the DBM data and the satellite image in the classification process to study the effect of applying the DBM on the overall classification accuracy and also on the building classification accuracy. Several classification techniques were tested (Unsupervised Classification, Maximum likelihood, Mahalanobis distance and Minimum distance) and the results were tabulated. The results demonstrate the potential of applying the proposed method to urban building feature extraction and updating.