Bin picking is one of the core tasks in robotics. In terms of automating industrial processes, bin-picking shows to be a fundamental step. Although highly researched, it is still a challenging task as it combines 3D object recognition and pose estimation, handling and path planning. This paper focuses on recognition and pose estimation. Here a concept for picking Lego blocks laying randomly in a bin using CAD models of objects to be handles and 3D point cloud is presented. The approach relies on aligning the point cloud captured by a Cognex camera with the CAD models, then uses a best-fit method to determine the gripping position and orientation of the object. The concept has been validated in an experimental setup and compared to a state-of-the-art tool. Results demonstrate the efficiency and robustness of the proposed approach.