The measurements of coordinated patterns of protein abundances using antibody microarrays could be used to gain insight into disease biology and to probe the use of combinations of proteins for disease classification. The correct use and interpretation of antibody microarray data requires proper normalization of the data, which has not yet been systematically studied. We therefore undertook a study to determine the optimal normalization of data from antibody microarray profiling of proteins in human serum specimens. Forty-three serum samples, collected from pancreatic cancer patients and controls, were probed in triplicate on microarrays containing 48 different antibodies, using a direct-labeling, two-color comparative fluorescence detection format. Seven different normalization methods, representing major classes of normalization for antibody microarray data, were compared by their effects on reproducibility, accuracy, and trends in the data set. Normalization with ELISA-determined concentrations of IgM resulted in the most accurate, reproducible and reliable data. The other normalization methods were deficient in at least one of the criteria. Multi-parametric classification of the samples, based on the combined measurement of seven of the proteins, demonstrated the potential for increased classification accuracy as compared to the use of individual measurements. This study establishes reliable normalization for antibody microarray data, criteria for assessing normalization performance, and the capability of antibody microarrays for serum-protein profiling and multiparametric sample classification.