Animal behaviors are reflective of its welfare state. They contain important information that can enable producers to better manage livestock. Yet it is more difficult in recognizing the behaviors of group laying hens than other big size animals. Large numbers of hens, homogeneous in appearance, high stocking density and variable body size all contribute to this situation. A computer vision-based system was developed which can automatically recognize group behaviors (distribution index, horizontal activity index and vertical activity index) and individual behaviors (feeding, lying, standing and sitting) of group hens. The system consisted of a 3D camera that simultaneously acquired digital and depth images and a software program that detected and identified the behaviors. The computational algorithm for the analysis of depth images was presented and its performance in recognizing the behaviors as compared with manual recognition was analyzed. The images were acquired at 5s intervals in 10d period. The algorithm had the following accuracy of individual behavioral classification: 90.3% in feeding, 91.5% in lying, 87.5% in standing and 56.2% in sitting. The lower classification accuracy for the sitting presumably stemmed to imprecise segmentation valve value between sitting and standing and sometimes mistook hen’s standing behavior (exploring in ground) for sitting which could be improved in later test. Hence the reported system provided an effective way to automatically process and classify hen’s group and individual behaviors. This tool was conducive to investigate behavioral responses and time budget of laying hens and facility design and management practice.