We developed a new computer-aided diagnostic (CAD) system for coronary artery stenosis, which can learn physicians’ diagnosis. To realize such a system, we developed a linear-output artificial neural network (LOANN) which is capable of learning of experts’ judgment. Our CAD system consisted of (a) an automated method for tracing vessels, (b) a robust method for determining the edges of the traced vessels, and (c) the LOANN for estimating stenosis based on the information obtained from the segmented vessels. To evaluate the performance of our CAD system, we carried out an experiment with vessel phantoms that had simulated stenoses. After the acquisition of the vessel phantom images with a digital subtraction angiography (DSA) system, we measured actual transverse areas of the vessel phantoms to determine stenoses. In a leave-one-out cross-validation test, our CAD system accurately estimated the stenoses determined based on the actual measurement of the vessel phantoms. To evaluate the performance of our CAD system on clinical cases, we used angiograms acquired with a DSA system. The stenoses estimated by our CAD system agreed well with those diagnosed by an experienced cardiologist. Therefore, our CAD system which has the capability to learn physicians’ diagnosis would be useful for assisting their diagnosis.