Current approaches to visual object class detection mainly focus on the recognition of abstract object categories, such as cars, motorbikes, mugs and bottles. Although these approaches have demonstrated impressive performance in terms of recognition, their restriction to abstract categories seems artificial and inadequate in the context of embodied, cognitive agents. Here, distinguishing objects according to functional aspects based on object affordances is vital for a meaningful human-machine interaction. In this paper, we propose a complete system for the detection of functional object classes, based on a representation of visually distinct hints on object affordances (affordance cues). It spans the complete cycle from tutor-driven acquisition of affordance cues, one-shot learning of corresponding object models, and detecting novel instances of functional object classes in real images