Data mining techniques presented in the literature are usually used for prediction and they are tested on well known benchmark problems. System identification is a practical engineering problem and an abductive task which is affected by several kinds of modeling assumptions and measurement errors. Therefore, system identification is supported by multiple-model reasoning strategies. The objective of this work is to study the use of data mining techniques for system identification. One goal is to improve views of model-space topologies. The presence of clusters of models having the same characteristics, thereby defining model classes, is an example of useful topological information. Distance metrics add knowledge related to cluster dissimilarity. Engineers are thus better able to improve decision making for system identification.