point independent recognition of free-form objects and estimation of their exact position are a complex procedure with applications in robotics, artificial intelligence, computer vision and many other scientific fields. In this paper a novel approach is presented that addresses recognition of objects lying in highly cluttered and occluded scenes. The proposed procedure relies on distance maps, which are extracted and stored off-line for each of the 3D objects that might be contained in the scene. During the on-line recognition procedure distance maps are extracted from the scene. Greyscale images, derived from scene’s distance maps, are matched with those of the object under recognition by applying similarity measures to the descriptors that are extracted from the images. The similarity is then estimated from image patches, which are defined using the SIFT descriptor in an appropriate way. After finding the best similarities the position of the object in the scene is estimated. This process is repeated until all objects are successfully recognized. Multiple experiments, which were performed on both 2.5D synthetic and real scenes, proved that the proposed method is robust and highly efficient to a satisfactory degree of occlusion and clutter.