This paper presents algorithms that identify objects in simple and complex images. The scene may contain objects that may touch or overlap giving rise to partial occlusion, objects in noisy environments, and objects from different domain giving potential to a general-purpose multi-domain object recognition. The algorithms are based on the idea of aggregating the total evidence for each object in the image utilizing Dempster-Shafer reasoning. The outline of the proposed approach consists of three processes: prepro-cessing, feature extraction, and understanding. As a step of the understanding process, the evidences related to each object in the image with corresponding models from the image domain are evaluated based on Dempster-Shafer theory of evidence. For this purpose, mass of evidence functions are derived and implemented. The algorithms are either bottom-down or bottom-up control. The later control makes the approach attractive in applications where the time is an important consideration such as robot vision, part inspection, and military target recognition. Experimental results are presented for simple and complex images.