Robust techniques for object recognition and pose estimation are essential for robotic manipulation and object grasping. In this paper, a novel approach for object recognition and pose estimation based on color cooccurrence histograms and geometric modelling is presented. The particular problems addressed are: (i) robust recognition of objects in natural scenes, (ii) estimation of partial pose using an appearance based approach, and (iii) complete 6DOF model based pose estimation and tracking using geometric models. Our recognition scheme is based on the color cooccurrence histograms embedded in a classical learning framework that facilitates a 'winner-takes-all' strategy across different views and scales. The hypotheses generated in the recognition stage provide the basis for estimating the orientation of the object around the vertical axis. This prior, incomplete pose information is subsequently made precise by a technique that facilitates a geometric model of the object to estimate and continuously track the complete 6DOF pose of the object. Major contributions of the proposed system are the ability to automatically initiate an object tracking process, its robustness and invariance towards scaling and translations as well as the computational efficiency since both recognition and pose estimation rely on the same representation of the object. The performance of the system is evaluated in a domestic environment with changing lighting and background conditions on a set of everyday objects.