False discovery rate (FDR) analyses of protein and peptide identification results using decoy database searching conventionally report aggregate or global FDRs for a whole set of identifications, which are often not very informative about the error rates of individual members in the set. We describe a nonlinear curve fitting method for calculating the local FDR, which estimates the chance that an individual protein (or peptide) is incorrect, and present a simple tool that implements this analysis. The goal of this method is to offer a simple extension to the now commonplace decoy database searching, providing additional valuable information.