Cardiac auscultation that is a still widely used technique to diagnose heart murmurs induced by heart disorders. Due to the fact that this method is quite subjective and time consuming, the enhancement of diagnosis techniques would contribute significantly to clinical auscultation. Development of computer-aided auscultative diagnosis systems, which provide more objective, reliable and faster results, would reduce the classification errors that may be occurred in the cardiovascular disorder diagnosis. Such an automated auscultative diagnostic software can be implemented by using signal processing and machine learning algorithms. The presented study uses a combination of the Walsh-Hadamard transform (WHT) and Hidden Markov Model (HMM) techniques. This study clearly shows that; successful automatic murmur diagnosis kits can be developed for assisting the doctors in clinical decision making process.