When inspecting a manufactured part to determine whether it falls within tolerance, it is often important to distinguish the difference between a defect and geometric variation. A part that exbibits excessive geometric variation will fail to meet the specifications of the design, such as a nominally flat surface that is bowed in the middle or a sphere that is not perfectly round. By comparison, a part that contains defects may meet the design tolerances, but contains imperfections that can range in severity from a cosmetic deformity to one that completely compromises the utility of the part. The role of defect detection in the manufacturing process is to make that distinction between the presence of a defect and geometric variation, and to classify the severity of detected defects so an educated decision can be made about whether the part is suitable for use. While custom applications can be utilized in some environments for defect classification and detection, they can be expensive and time consuming to create and maintain. Due to this issue, there are a variety of commercial applications that have been created in an attempt to streamline and simplify the defect workflow. The ATOS Professional application is one such software, and has the advantage of being directly integrated with the GOM family of measuring instruments. The goal of this project is to gain a thorough understanding of the defect detection and classification tools available in ATOS Professional 2019, and provide documentation for use of these tools in future projects. To assist in this study, an artifact with manufactured defects was created in the UNC Charlotte machine shop. A full investigation of this artifact is accomplished by changing the parameters of the surface defect map and surface defect classification tools in ATOS Professional 2019. The data are then analyzed to make recommendations based on the ideal parameters for this artifact, and these parameters are validated on other artifacts to see how well they translate. Through this research, no correlation was found between the four main variables used to create the defect map when considering the percent error, but a correlation was present between the variables and potential false positives detected. Orientation and inversion were also found to have a small, yet noticeable effect on the results of both the defect map and surface defect classification tools. The parameters determined for the defect artifact translated well to other manufactured artifacts, but were considerably less successful when applied to real world "organic" artifacts. It is recommended based on this research to utilize the ATOS Professional defect toolset on artifacts with relatively flat surfaces with no dominant surface structure. Careful attention should be paid to the quality of the mesh and orientation of the part in the software prior to analysis. The surface defect map tool should have the maximum defect size parameter set to approximately the width of the widest anticipated defect of interest on the part, the number of directions parameter set to 1, and the type parameter set to depressions only or bulges only, not both. The XYZ parameter should be set based on the orientation of the part.