A key problem in multimedia systems is the faithful reproduction of color. One of the main reasons why this is a complicated issue is the different color reproduction technologies used by the various devices; displays use easily modeled additive color mixing, while printers use a subtractive process, the characterization of which is much more complex than that of self-luminous displays. To resolve these problems several processing steps are necessary, one of which is accurate device characterization. We examine different learning algorithms for one particular neural network technique that already has been found to be useful in related contexts—namely, radial basis function network models—and propose a modified learning algorithm that improves the colorimetric characterization process of printers. In particular our results show that it is possible to obtain good performance using a learning algorithm that is trained on only small sets of color samples, and use it to generate a larger look-up table (LUT) through the use of multiple polynomial regression or an interpolation algorithm. We deem our findings to be a good starting point for further studies on learning algorithms used in conjunction with this problem.