The multivariate analysis techniques of principal components analysis (PCA), principal component regression (PCR), and partial least squares regression (PLSR) were used to calibrate time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) data against X‐ray photoelectron spectroscopy (XPS) data obtained from plasma‐treated polypropylene. This establishes correlations between quantitative information obtained from XPS with the molecular information indicated by ToF‐SIMS, allowing the relative concentration of CO functional groups and C:O atomic concentration ratio on the surfaces of plasma‐treated polypropylene to be predicted from ToF‐SIMS data alone. A four‐factor prediction model was constructed, and was deemed as adequate to predict the concentrations of the surface CO functional groups, and of the C:O atomic ratio with root mean square error of prediction (RMSEP) values of 0.445 and 0.671 at%, respectively. ToF‐SIMS data with semi‐quantitative XPS data were correlated in a model that can be used to predict chemical properties quantitatively. A model constructed using PLSR with XPS and ToF‐SIMS data obtained from the same plasma‐treated polypropylene surfaces showed high calibration and prediction ability. Low RMSEP values for the concentrations of CO groups and C/O ratio were obtained using a 4‐component PLSR model.