A patient's intensive care unit (ICU) length of stay following cardiac surgery is an important issue in Canada, where cardiovascular intensive care resources are limited and waiting lists for cardiac surgery exist. We trained a neural network with a database of 713 patients and 15 input variables to predict patients who would have a prolonged ICU length of stay, defined as a stay greater than 2 days. In an independent test set of 696 patients, the network was able to stratify patients into three risk groups for prolonged stay (low, intermediate, and high), corresponding to frequencies of prolonged stay of 16.3, 35.3, and 60.8%, respectively. The trained network could potentially be used as a predictive instrument for optimizing the scheduling of cardiac surgery patients in times of limited ICU resources. Neural networks are a new method for developing predictive instruments that offer both advantages and disadvantages when compared to other more widely used statistical techniques.