Surface monitoring is an essential part of quality assurance for additive manufacturing (AM). Surface defects need to be identified early in the AM process to avoid further deterioration of the part quality. In this paper, a rapid surface defect identification method for directed energy deposition (DED) is proposed. The main contribution of this work is the development of an in-situ point cloud processing with machine learning methods that enable automatic surface monitoring without sensor intermittence. An in-house software platform with a multi-nodal architecture is developed. In-situ point cloud processing steps, including filtering, segmentation, surface-to-point distance calculation, point clustering, and machine learning feature extraction, are performed by multiple subprocesses running simultaneously. The combined unsupervised and supervised machine learning techniques are applied to detect and classify surface defects. The proposed method is experimentally validated, and a surface defect identification accuracy of 93.15% is achieved.