This paper introduces a novel approach to learn representations of objects using a team of robots. Each robot extracts local and global visual features of objects and combines them to represent and recognize objects. Contrary to previous approaches the robots do not know in advance the number or nature of objects to learn. Individual representations of objects are learned on-line while the robots are traversing an environment. Robots share their individual concepts to improve their own concepts, and to acquire a new representation of an object not seen by them. For that, the robots have to detect if they are seeing a new object or an already learned one. We empirically evaluated our approach with a real world robot team with very promising results.