Multiple sclerosis is a demyelinating disease of the central nervous system with a presumed autoimmune etiology. Previous microarray analyses identified conserved gene expression signatures in peripheral blood mononuclear cells of patients with autoimmune diseases. We used quantitative real-time polymerase chain reaction analysis to identify a minimum number of genes of which transcript levels discriminated multiple sclerosis patients from patients with other chronic diseases and from controls. We used a computer program to search quantitative transcript levels to identify optimum ratios that distinguished among the different categories. A combination of a 4-ratio equation using expression levels of five genes segregated the multiple sclerosis cohort (n=55) from the control cohort (n=49) with a sensitivity of 91% and specificity of 98%. When autoimmune and other chronic disease groups were included (n=78), this discriminator still performed with a sensitivity of 79% and a specificity of 87%. This approach may have diagnostic utility not only for multiple sclerosis but also for other clinically complex autoimmune diseases.