In this study, the problem of updating a printer characterization in response to systematic changes in print-device characteristics is addressed with two distinct approaches: the creation of corrective models used in conjunction with an existing device model, and the re-evaluation of regression-model parameters using an augmented characterization data set. Several types of corrective models are evaluated, including polynomial models and neuralnetwork models. A significant reduction in error was realized by incorporating these techniques into the color-management program NeuralColor. The most successful of these methods was a quadratic polynomial correction model, which removed 90% of the error introduced by a change of paper stock, and all of the error introduced by a change in toner cartridge. A general conclusion is that simple corrective models exhibiting global control are preferred over more complex models which may introduce local errors.