We report on the construction of neural networks for determining whether pediatric patients requiring transport to a tertiary care center should be moved by air or by ground. The networks were based on the functional-link net architecture. In two experiments, feedforward supervised-learning neural nets were trained with examples of an expert's decisions and then were used in a consulting mode to provide advice on cases not previously encountered. Training and validation were performed by a combination of the k-fold cross-validation and leaving-one-out sampling methods. Use of the functional-link net rather than the customary backpropagation net enabled us to carry out the training with fairly large amounts of data in realistically short time periods. In the first experiment, capillary refill, skin color, and stridor were consistently the input variables that were most strongly associated with the decision output. In both experiments, the networks were validated by comparing their performance retrospectively against the determination of an expert pediatric transport physician. The network was trained based on the expert's opinion about the correct mode of transport for each case with error rates of less than 10(-5).