A condition of overtraining a back-propagation neural network exists when excessive model degrees of freedom are used in network training. A CRT color calibration experiment was done to illustrate methods to avoid an overtrained condition in model development. Cross-validation, in which the experimental data are split into parameter-training and independent-test data sets, is advocated.