A method is proposed for surface defect analysis and evaluation. Good 3D point clouds can now be obtained through a variety of surface profiling methods such as stylus tracers, structured light, or interferometry. In order to inspect a surface for defects, first a reference surface that represents the surface without any defects needs to be identified. This reference surface can then be fit to the point cloud. The algorithm we present finds the least square solution for the overdetermined equation set to obtain the parameters of the reference surface mathematical description. The distance between each point within the point cloud and the reference surface is then calculated using to the derived reference surface equation. For analysis of the data, the user can preset a threshold distance value. If the calculated distance is bigger than the threshold value, the corresponding point is marked as a defect point. The software then generates a color-coded map of the measured surface. Defect points that are connected together are formed into a defect-clustering domain. Each defect-clustering domain is treated as one defect area. We then use a clustering domain searching algorithm to auto-search all the defect areas in the point cloud. The different critical parameters used for evaluating the defect status of a point cloud that can be calculated are described as: P-Depth,a peak depth of all defects; Defect Number, the number of surface defects; Defects/Area, the defect number in unit area; and Defect Coverage Ratio which is a ratio of the defect area to the region of interest.