Tests have shown that high-dispersion spectra from each of the three IUE cameras have characteristics that impose unique challenges for automated background extraction algorithms. Examples of specific problems are given in Appendix A. The great diversity of image types in the archives prohibits implementing any strategy that makes assumptions about the behavior of source spectra in order to fix isolated background problems, particularly in an automated processing environment. Although the background-extraction algorithm generally provides a good background flux estimate, the results are not always optimal for particular regions of some images. A customized interactive determination of the background fluxes based on individual image characteristics may produce a more accurate estimate of the background in certain cases when data pathologies are present. If users wish to derive a customized background, they should first reconstitute the ``gross'' flux spectrum by adding the background vector to the net flux vector, multiply the customized vector by the conversion factor between the high-dispersion SI and merged extracted image (MX) fluxes (slit_length*32.0), and subtract this result from the gross spectrum. (In this example, the customized background vector is assumed to have been derived on the basis of a 1-pixel long slit.)