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We present a study on the effectiveness of computer-aided diagnosis (CAD) of rheumatoid arthritis (RA) from frequency-domain diffuse optical tomographic (FDOT) images. FDOT is used to obtain the distribution of tissue optical properties. Subsequently, the non-parametric Kruskal-Wallis ANOVA test is employed to verify statistically significant differences between the optical parameters of patients affected by RA and healthy volunteers. Furthermore, quadratic discriminate analysis (QDA) of the absorption (a) and scattering (aor 's) distributions is used to classify subjects as affected or not affected by RA. We evaluate the classification efficiency by determining the sensitivity(Se), specificity(Sp), and the Youdenindex (Y). We find that combining features extracted from aand aor 'simages allows for more accurate classification than when aor aor 'sfeatures are considered individually on their own. Combining aand aor 'sfeatures yields values of up to Y= 0.75 (Se= 0.84 and Sp= 0.91). The best results when aor 'sfeatures are considered individually are Y= 0.65 (Se= 0.85 and Sp= 0.80) and Y= 0.70 (Se= 0.80 and Sp= 0.90), respectively.