Object recognition depends mainly on extracting the optimal features which should be insensitive to image translation, scaling, rotation, and noise. However, it is a complicated and difficult process, and stable features may not be extracted in some cases. In this paper, a novel approach to object recognition based on a single sensor is proposed in the view of data fusion and Dempster-Shafer's theory. The notions of image subfeature and similar degree function (SDF) are first introduced. For each SDF function, we further establish a set of subordinate functions (SF). The SDF and SF are combined in the fusion model. For each class of training samples, several subfeatures are selected by the different methods. Then, the SDF and a set of SF functions are calculated. Finally, the Dempster's rule of combination is used and all subfeatures are fused. In the fusion model, a simple classifier is designed to recognize objects. Experimental results show that the proposed method is efficient and our recognition model has good performance.