We present a novel method of statistical analysis for the comparison of electrophoretic data. The method is based on the squared Euclidian distance of normalized signal data vectors of electrophoretic lanes. The differences in the electrophoretic patterns are evaluated by a statistical test based on Hubert's statistics which measures the significance of the signal grouping. We demonstrate the validity and applicability of the method in a large data set derived from automated fluorescent mRNA differential display analysis of the expression of acute-phase proteins during experimental Escherichia coli infection in mice. The current testing method is capable of finding theoretically similar natural groupings to be similar in a statistically significant way whereas theoretically dissimilar or random groupings can be recognized to be artifactual. We also show how the calculated pairwise signal distances can be utilized in methodological problem solving. These analytical methods can be applied to the study of other related problems of similarity analysis of electrophoretic patterns, and also provide useful tools for the development of automated recognition of differentially expressed mRNAs.