The rapid accurate diagnosis of critical disorders is an essential component of intensive care. Traditional diagnostic techniques have relied on physician experience, which is based on a data set chosen from his or her personal preferences, rather than from scientific merit. In this article, we show that there are alternative methods of selecting clinical variables on which to base a diagnosis. We suggest that a model-based technique utilizing artificial neural networks (ANNs) can be used to investigate alternative, objectively chosen data input sets. Traditionally, ANNs have been used for diagnosis or prediction tasks; however, this article introduces a novel method of exploring the inner structure of suitably trained ANNs to determine a set of key variables for each clinical state defined. Two different ANN techniques are proposed: self-organizing maps and backpropagation networks. We do not claim that these techniques provide the optimal data set for decision making, but we do show that other combinations of data exist that may be an improvement over the physician methods currently used.