We have developed a novel large-scale multicapillary fluorescent differential display (FDD) platform amenable to further automation. The power of the method is demonstrated by the analysis of T helper cell differentiation. Eight RNA samples from wild type, Stat4 knockout and Stat6 knockout mice were analyzed with 16 anchoring primers and 24 arbitrary primers, resulting in 285 294 sample peaks. Visually selected patterns of differential expression suggest two major regulatory mechanisms: activation and Stat4 genotype. A subset of the findings is reproduced in the confirmatory differential display (DD) that included technical and biological replicates. In a small fragment identification pilot study, we identify Ifi27 and Cct8 to be up-regulated by T cell activation. We present a method for the analysis of electropherogram. similarity across large datasets, based on correlation of low-resolution representations of electrophoretic data. We show how it can be applied to analyze experimental and technical variables. Using this method, we demonstrate the effect of activation and genotype. In addition, agreement of our real experimental data to the theoretical basis of DD, as well as issues in anchoring primer selectivity, are studied.