As an urban subject, the extraction of buildings has been a hot topic for scholars. Airborne laser scanning data collected in urban areas have a huge data volume and numerous objects with complex and incomplete structures which raise a great challenge for automatic extraction of buildings. To address this challenge, we propose an algorithm based on object-oriented decision tree to extract buildings with high precision. It can handle multiple attributes simultaneously and be unaffected by missed points. First, combined with mean elevation and neighbor within each laser point, judging criteria in each internal node are determined to generate building points by using the relationship between each object attribute and its corresponding eigenvalues. Next, through comparing the entropy from all dataset features, an optimal feature and value candidate are chosen to get a correct classifier with supervised learning to apply to the dataset to be processed. The experimental results show that the proposed method is capable of extracting building points from the airborne laser scanning data with a high accuracy of above 96%.