An automatic cow health monitoring and management system cannot function effectively without an identification system in livestock farming. While 2D image-based computer vision currently achieves high accuracy in cow identification, its effectiveness can be significantly decreased by changes in lighting, environmental factors, and other limitations. To address these limitations, an identification system based on point-cloud images will be developed by using a combination of 3D TOF camera and 2D RGB camera. This system includes detection and segmentation stage, feature extraction stage, and identification stage. In this study, I focus on detecting and segmenting of cow back surface region from a point-cloud image using the PointNet+ + algorithm. Two segmentation models are trained and compared based on single-scale grouping (SSG) and multi-scale grouping (MSG) features. The extracted cow back surface region offers a rich set of features valuable for several applications, including individual cow identification, lameness detection, and body condition scoring.