Future computer vision systems must have the ability to discover new object models. This problem can be addressed by relational concept formation systems, which structure a stream of observations into a taxonomy of discovered concepts. This paper presents a representation for images which is invariant under arbitrary groups of transformations. The discovered models, also being invariant, can be used as indices for 3D images. The methodology is illustrated on a small problem in molecular scene analysis, where discovered models, invariant under Euclidean transformations, are efficiently recognized in a cluttered molecular scene.