In this thesis, we present an improvement on the Expectation Propagation learning framework, specifically various enhancements on both speed and accuracy. We use this enhanced EP learning with the Inverted Dirichlet mixture model as well as the Dirichlet mixture model, to implement an algorithm to recognize 3D objects. Those objects are in our case from a view-based 3D models database that we have assembled. Following specific rules determined by analyzing the results of our tests, we’ve been able to get good recognition rates. Experimental results are presented with different object classes by comparing recognition rates and confidence level, according to different tuning parameters we’re able to refine towards specific classes for better specialized accuracy.