Boosting for generic 2D 3D object recognition Generic object recognition is an important function of the human visual system. For an artificial vision system to be able to emulate the human perception abilities it should also be able to perform generic object recognition. In this thesis we address the generic object recognition problem and present different approaches and models which tackle different aspects of this difficult problem. First we present a model for generic 2D object recognition from complex 2D images. The model exploits only appearance based information in the form of a combination of texture and color cues for binary classification of 2D object classes. Learning is accomplished in a weakly supervised manner using Boosting. However we live in a 3D world and the ability to recognize 3D objects is very important for any vision system. Therefore we present a model for generic recognition of 3D objects from range images. Our model makes use of a combination of simple local shape descriptors extracted from range images for recognizing 3D object categories as shape is an important information provided by range images. Moreover we present a novel dataset for generic object recognition that provides 2D and range images about different object classes using a Time of Flight (ToF) camera.