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A pattern recognition system for computer classification of cortisol time series into the normal class and subclasses of Cushing's syndrome with different etiologies was developed. Discriminatory features are extracted from Fourier analysis and Karhunen-Loeve expansion coefficient or cortisol time series. Decision functions are trained by the least mean square error (LMSE) algorithm and tested by the jackknife test procedure on a database of 90 normal and patient patterns. The classification accuracy for normal, pituitary, adrenal, and ectopic classes is 100. 98.1, 98.3. and 100%, respectively. Hence, this pattern recognition system may be useful as an aid in the differential diagnosis of Cushing's syndrome. Twenty-four-hour 24-h cortisol patterns can be easily obtained in a clinical research unit. This recognition system can be upgraded as new time series become available.