Sensitivity and specificity are key measures of the performance of a given test in detecting a given disorder. For tests yielding numerical scores, sensitivity and specificity usually vary inversely over the range of theoretically possible cutoff scores, complicating the task of quantifying and comparing the diagnostic accuracy of tests. Receiver Operating Characteristic analysis (ROC) approaches this problem by plotting the curve of sensitivity versus 1-specificity for all possible cutoff scores of the test. The area under the ROC curve (AUC) can be used to describe the diagnostic accuracy of the test. Parametric and non-parametric methods exist that allow the calculation of the AUC and the comparison of tests. A disadvantage of parametric formulations is the assumption of a normal or Gaussian distribution of test scores. The present article presents a computer program that utilizes non-parametric formulations that do not require the normal distribution of test scores. The program calculates the sensitivity and specificity of a test at all possible cutoff scores, plots the ROC curve, calculates the AUC, its standard error and 95% confidence limits, and allows the comparison of tests on independent and correlated samples.