The application of infrared microspectroscopy (IRMS) technology, combined with multivariate analysis, was evaluated to develop sensitive and robust methods to assess cleanability of stainless steel surfaces for the removal of dairy food residues. UHT milk samples (skim, 1%, 2%, and whole) were analyzed for total nitrogen (Kjeldahl) and fat (Babcock) contents. The coupons were manually soiled with serially diluted milk samples resulting in soils ranging from 0.1 to 428.1 μg/cm2 for protein and 0.1 to 374.17 μg/cm2 for fat, and then autoclaved to simulate a heated equipment surface. Reflectance spectra were collected from stainless steel coupons by using IRMS, and multivariate analysis was used to develop calibration models based on cross-validated partial least squares regression (PLSR). Statistical analysis for the prediction of protein and fat showed a standard error of cross-validation (SECV) of 0.5 and 0.4 μg/cm2 for prediction of protein and fat, respectively, and correlation coefficients (rVal) > 0.99. To improve the sensitivity, swabbing and concentration steps were used prior to IRMS analysis obtaining SECV of 0.04 and 0.01 μg/cm2 for the prediction of protein and fat, respectively, and rVal > 0.99. The PLSR models accurately predicted the levels of protein and fat on autoclaved stainless steel coupons soiled with milk. A simple, reliable, and robust protocol based on IRMS and multivariate analysis was developed for multicomponent characterization of stainless steel surfaces that can contribute to more efficient cleaning verification with regard to contamination on surfaces of processing equipment. © 2011 Institute of Food Technologists®.