The connectionist model lAK (Information evaluation using configurations) for classification learning is presented here. The model can be placed between feature based (e.g. Gluck & Bower, 1988) and exemplar based models (e.g. ALCOVE , Kruschke, 1992). Specific to this model is that during learning, sets of input features are probabilistically sampled. These sets are represented, in a hidden layer, by configuration nodes. These configuration nodes are connected to output nodes that represent category labels. A further characteristic of the lAK model is a mechanism which enhances retrieval of information. Simulations with the lAK model can explain different phenomena of classification learning which have been found in experimental studies: A Type 2 advantage without dimensional attention learning observed by Shepard et al. (1961); a generalisation of prototypes; a generalization based on similarity to learned exemplars; a differential forgetting of prototypes and exemplars; a moderate interference (fan effect) caused by stimulus similarity; and the missing of catastrophic interference even in A-B/A-Brdesigns.