In the intricate process of shipbuilding, digitalization plays a significant role in enhancing competitiveness and sustainability. This multifaceted process involves numerous minor pre-assemblies, where automation, specifically through robotic welding, proves instrumental in enhancing overall efficiency. This paper utilizes real data from a shipyard to implement an artificial vision system designed to identify the elements required for assembling a minor pre-assembly. Surface-based matching and 3D edge matching techniques were applied to a diverse dataset of point cloud images. By employing multiple computer-aided design models to generate corresponding surface models, we successfully identified all the pieces present on a tray. This identification process is particularly complex due to the multitude of minor pre-assembly types, the challenge of distinguishing between similar components, and the stacking of these parts. The distinction of these elements significantly contributes to ensuring traceability throughout the hole shipbuilding process.