Although most PCRs would produce proper PCR products when first tried, a general optimization is required to yield the best results. This optimization is often achieved by changing one factor at a time. However, this may lead to suboptimal results, since interactions between conditions are difficult to detect with this approach. In the present study, we describe the factorial optimization of PCR conditions for microsatellite genotyping, by introducing small systematic variations in conditions during the genotyping process. The hypothesis was that small changes will not affect genotyping results, but will provide information about the optimality of current conditions. The conditions to vary were the concentrations of buffer, MgCl(2), dNTPs, primers, Taq polymerase and DNA, the annealing temperature (T(a)) and the number of cycles. We show that, by a 2(8) factorial experiment, it is possible to identify not only the factors responsible for obtaining good results, but also those responsible for bad results. However, the condition leading to the highest signals is not necessarily the best operational condition. The best operational condition is minimally sensitive to random pipetting fluctuations and yields reliable genotypes as well.