When building an application that requires object class recognition, having enough data to learn from is critical for good performance, and can easily determine the success or failure of the system. However, it is typically extremely laborintensive to collect data, as the process usually involves acquiring the image, then manual cropping and hand-labeling. Preparing large training sets for object recognition has already become one of the main bottlenecks for such emerging applications as mobile robotics and object recognition on the web. This paper focuses on a novel and practical solution to the dataset collection problem. Our method is based on using a green screen to rapidly collect example images; we then use a probabilistic model to rapidly synthesize a much larger training set that attempts to capture desired invariants in the object’s foreground and background. We demonstrate this procedure on our own mobile robotics platform, where we achieve 135x savings in the time/effort needed to obtain a training set. Our data collection method is agnostic to the learning algorithm being used, and applies to any of a large class of standard object recognition methods. Given these results, we suggest that this method become a standard protocol for developing scalable object recognition systems. Further, we used our data to build reliable classifiers that enabled our robot to visually recognize an object in an office environment, and thereby fetch an object from an office in response to a verbal request.