Two Spectroscopic Data-Activity Relationship (SDAR) models based on (13)C nuclear magnetic resonance (NMR) and electron ionization mass spectra (EI MS) data were developed for 108 compounds whose relative binding affinities (RBA) to the estrogen receptor are known. The (13)C NMR and EI MS data were used as spectrometric digital fingerprints to reflect the electronic and structural characteristics of the compounds. Both SDAR models segregated the 108 compounds into 20 strong, 15 medium, and 73 weak relative binding classifications. The first SDAR model, based on (13)C NMR data alone, gave a leave-one-out (LOO) cross-validation of 75.0%. The second SDAR model, based on a composite of (13)C NMR and EI MS data, gave a LOO cross-validation of 82.4%. Many of the misidentifications from the cross-validations were between medium and weak classifications, where there were fewer specific spectrometric characteristics to identify the relationship of spectra to estrogen receptor binding. Real and predicted (13)C NMR chemical shifts were used to test the predictive behavior of both SDAR models. The ease of use and speed of SDAR modeling may facilitate their use with other toxicological endpoints.