Multi-sensor fusion is widely used in object recognition and classification at present, since this technique can efficiently improve the accuracy and the ability of fault tolerancy. This current presentation described a sub-system of multi-sensor integration: range and intensity image fusion system, which is model-based and used for object recognition and classification. In the data fusion process, the Dempster-Shafer's Evidential Reasoning (DSER) is selected and used for the data fusion at report level. This presentation mainly discussed the construction of the Basic Probability Assignments Function in DSER for the recognition and classification, and the decision strategies based on the fused information. At the same time, by comparing the experimental results based on separate original data and the fused data respectively, it is shown that the latter is more accurate than the first ones.