Object recognition in a household environment performed by a service robot. The objects are recognized by shape, texture and color-based algorithms whose outputs are then combined to create an object hypothesis. This works score-based, meaning that arbitrary algorithms and recognition frameworks can be combined without changing their outputs. In contrary to similar approaches, the proposed method automatically selects which algorithms to use and how to weigh their outputs. Reference: T. Fromm, B. Staehle, W. Ertel: Robust Multi-Algorithm Object Recognition Using Machine Learning Methods. IEEE International Conference on Multisensor Fusion and Information Integration, Hamburg, Germany, 2012.