We too have experienced difficulties in fitting backgrounds to systems with strong edge features. Playing with the edge parameters sometimes works ok, but it's not a robust procedure. Thus we have attempted to use the theoretical mu_0 from FEFF as an a priori, and spline corrections on top of that. Thus the form of mu_0 would be: mu_0 = mu^thy_0(E,E_0,Gamma)[1 + lambda(E)] where the theoretical mu^thy_0 has an adjustable edge position (E_0) and broadening (Gamma), and lambda(E) is the spline correction which includes both instrumental variations with E and theoretical errors. In our experience, the FEFF8 mu^thy_0 can often give a good approximation to mu_0, even near the edge where there are large white lines. We've tested this on several systems, and the approach does seem promising, though it requires the extra step of running FEFF in the XANES region. In fact one has to run FEFF more than once if one tries to fit the edge parameters. A brief description is in a paper which should be out soon: ``Bayes-Turchin Approach to XAS Analysis," J. J. Rehr, J. Kozdon, J. Kas, H. J. Krappe, and H. H. Rossner, J. Synchrotron Radiation (in press, 2004). On the other hand the approach can be automated and can give a simultaneous fit of both EXAFS and XANES. We'd be interested in comments/suggestions on this approach. For example, how best to represent small corrections to the broadened edge step (e.g., arctangent corrections). John Rehr Joshua Kas