In this paper we propose an approach capable of si- multaneous recognition and localization of multiple object classes using a generative model. A novel hierarchical rep- resentation allows to represent individual images as well as various objects classes in a single, scale and rotation invari- ant model. The recognition method is based on a codebook representation where appearance clusters built from edge based features are shared among several object classes. A probabilistic model allows for reliable detection of various objects in the same image. The approach is highly effi- cient due to fast clustering and matching methods capable of dealing with millions of high dimensional features. The system shows excellent performance on several object cate- gories over a wide range of scales, in-plane rotations, back- ground clutter, and partial occlusions. The performance of the proposed multi-object class detection approach is com- petitive to state of the art approaches dedicated to a single object class recognition problem.