Building knowledge for robots can be tedious, especially if focused on object class recognition in home environments where hundreds of everyday-objects - some with a huge intra class variability - can be found. Object recognition and especially object class recognition is a key capability in home-robotics. Achieving deployable results from state-of-the-art algorithms is not yet achievable when the number of classes increases and near real-time is the goal. Hence, we propose to exploit contextual knowledge by using sensor and hardware constraints from the robotics and home domains and show how to use the internet as a source for obtaining the required data for building a fast, vision based object categorization system for robotics. In this paper, we give an overview of the available constraints and advantages of using a robot to set priors for object classification and propose a system which covers automated model acquisition from the web, domain simulation, descriptor generation, 3D data processing from dense stereo and classification for a - not too far - robot scenario in an internet-connected home-environment. In this work we show that this system is capable of being used in home robotics in a fast and robust way for recognition of object classes commonly found in such environments, including but not limited to chairs and mugs. We also discuss challenges and missing pieces in the framework and useful extensions.