Building facade is an important component of urban street features. Delineating and representing the building facade would benefit the urban building design and planning. As a new mobile mapping system, Mobile Laser Scanning (MLS) allows the quick and cost-effective acquisition of close-range three-dimensional (3D) measurements of urban street objects. This paper pres-ents a semiautomated segmentation method for identifying the building facades from MLS point clouds data. The method consists of three major steps:(1) a horizontal grid system is built for the study area, and the multidimensional geometric features of 3D point clouds data, including the normal vector feature, omni-variance feature, geometric dimensionality ofα1,α2 andα3, and eigen-entropy feature, are defined and calculated. Then, a feature image is created after projecting these features to the horizontal grid. (2) Build-ing facades are roughly extracted using Support Vector Machine (SVM). (3) The rough extraction result is filtered according to the characteristics of grid including the shape coefficient, grid′s area, and the largest elevation. Two MLS point cloud datasets of Carne-gie Mellon University (CMU) database were used in this study to estimate the feasibility and effectiveness of the method. It was found that this method performs well in extracting the building facades. The precision of the results is 0.88, and its recall rate is 0.90, which is better than some existing methods. Our method provides an effective tool for extracting building facades of streets from MLS point cloud data.