Estimation of false discovery rate (FDR) for identified peptides is an important step in large-scale proteomic studies. We introduced an empirical approach to the problem that is based on the FDR-like functions of sets of peptide spectral matches (PSMs). These functions have close values for equal-sized sets with the same FDR and depend monotonically on the FDR of a set. We have found three of them, based on three complementary sources of data: chromatography, mass spectrometry, and sequences of identified peptides. Using a calibration on a set of putative correct PSMs these functions were converted into the FDR scale. The approach was tested on a set of approximately 2800 PSMs obtained from rat kidney tissue. The estimates based on all three data sources were rather consistent with each other as well as with one made using the target-decoy strategy.