Multivariate techniques were used to address the quantification of (17)O-nuclear magnetic resonance (NMR) spectra for a series of primary alcohol mixtures. Due to highly overlapping resonances, quantitative spectral evaluation using standard integration and deconvolution techniques proved difficult. Multivariate evaluation of the (17)O-NMR spectral data obtained for 26 mixtures of five primary alcohols demonstrated that obtaining information about spectral overlap and interferences allowed the development of more accurate models. Initial partial least squares (PLS) models developed for the (17)O-NMR data collected from the primary alcohol mixtures resulted in very poor precision, with signal overlap between the different chemical species suspected of being the primary contributor to the error. To directly evaluate the question of spectral overlap in these alcohol mixtures, net analyte signal (NAS) analyses were performed. The NAS results indicate that alcohols with similar chain lengths produced severely overlapping (17)O-NMR resonances. Grouping the alcohols based on chain length allowed more accurate and robust calibration models to be developed.