Depression is one of the most prevalent mental health problems and measuring depressive symptoms becomes increasingly important in science as well as medical practice. Computer Adaptive Tests (CAT) based on the Item Response Theory (IRT) promise to enhance measurement precision and reduce respondent's burden. Our aim was to develop a CAT application to measure depressive symptoms. Three thousand two hundred seventy psychosomatic patients answered an overall of 11 mental health questionnaires at the University Clinic in Berlin. Three independent reviewers rated 144 items out of these questionnaires as indicative of depressive symptoms. All items underwent six empirical steps to analyze unidimensionality, local independence and item discrimination. Finally 64 items could be used to calculate item parameters applying a Generalized Partial Credit Model (GPCM). CAT scores were estimated using an 'expected a posteriori' algorithm (EAP). Two simulation experiments showed that for theta values within the range of 2SD around the mean (98% of the cases), the latent trait can be estimated out of approximately six items with a predefined standard error of [Symbol: see text] 0.32 (reliability rho [Symbol: see text] 0.90). The CAT-scores correlated high with scores of all depression items (r = 0.95), with the Beck Depression Inventory (r = 0.79) and with a CES-D 8 item short form (r = 0.76). We conclude that the Depression-CAT measures depressive symptoms with high precision and low respondent burden.