For natural and efficient verbal communication between a robot and humans, the robot should be able to learn names and appearances of new objects it encounters. In this paper we present a solution combining active sensing of images with text based and image based search on the Internet. The approach allows the robot to learn both object name and how to recognise similar objects in the future, all self-supervised without human assistance. One part of the solution is a novel iterative method to determine the object name using image classi- fication, acquisition of images from additional viewpoints, and Internet search. In this paper, the algorithmic part of the proposed solution is presented together with evaluations using manually acquired camera images, while Internet data was acquired through direct and reverse image search with Google, Bing, and Yandex. Classification with multi-classSVM and with five different features settings were evaluated. With five object classes, the best performing classifier used a combination of Pyramid of Histogram of Visual Words (PHOW) and Pyramid of Histogram of Oriented Gradient (PHOG) features, and reached a precision of 80% and a recall of 78%
