In this paper, we present a novel approach for multiple-feature, multiple-sensor classification and localization of three-dimensional objects in two-dimensional images. We use a hypothesize-and-test-approach where we fit three-dimensional geometric models to image data. A hypothesis consists of an object's class and its six degrees of freedom. Our models consist of the objects' geometric data which is attributed with several local features, e.g. hotspots, edges and textures, and their respective rule of applicability (e.g. visibility). The model-fitting process is divided into three parts: using the hypothesis we first project the object onto the image plane while evaluating the rules of applicability for its local features. Hence, we get a two-dimensional representation of the objects which - in a second step - is aligned to the image data. In the last step, we perform a pose estimation to calculate the object's six degrees of freedom and to update the hypothesis out of the alignment results. The paper describes the major components of our system. This includes the management and generation of the hypotheses, the matching process, the pose estimation, and model-based prediction of the object's pose in six degrees of freedom. At the end, we show the performance, robustness and accuracy of the system in two applications (optical inspection for quality control and airport ground-traffic surveillance).