This study presents an effective method for segmenting the skeleton of a three-dimensional point cloud. Segmentation results can be used in point cloud object recognition and classification. First, an L₁-central skeleton algorithm with strong robustness is used to extract the skeleton of point cloud data, and a series of skeleton points is obtained. Then, the skeleton points are segmented by using the octree based region growing segmentation method. The selected criteria are normal vectors and residuals. Finally, OpenGL is used to connect the segmented parts. Numerous experiments are conducted with point cloud data of various shapes, such as animal, plant, human, and alphabetic models, and good results are obtained.