Drug dosages and drug choices are determined by a knowledge of the drug's pharmacokinetics and pharmacodynamics. Often, insufficient information is available to determine the pharmacokinetics of a drug or which drug will have a desired effect for an individual patient. We propose that a form of nonlinear regression, an artificial neural network, can be used. We have demonstrated this use with 2 examples. In the first example we use a neural network to predict gentamicin peak and trough concentrations from routine therapeutic drug monitoring. In the second example we predict delayed renal allograft function as a guide for induction of immunosuppression therapy. Predictions were made using a multilayer feedforward perceptron and compared to nonlinear mixed effect modeling (NONMEM) and logistic regression. Neural network peak and trough gentamicin predictions were more precise and less biased than control predictions made using NONMEM. Prediction error for peak serum concentrations averaged 16.5% for the neural networks and 18.6% for NONMEM. Prediction error for trough concentrations were 48.3% for neural networks and 59.0% for NONMEM. When used for the prediction of delayed graft function, the neural network correctly predicted immediate graft function 73% of the time and delayed graft function 65% of the time. For those patients predicted to develop delayed graft function, alternate induction using anti-lymphocyte globulin may be indicated. These 2 examples demonstrate how an artificial neural network may be applied to predictions in clinical pharmacology.