We discuss appropriate feature models for the automatic diagnosis of faults in two different application scenarios in a comparative study. The first test case is the Case Western Reserve University Bearing Data, the second is a submersible pump used in offshore oil exploration. Additionally we provide a visual comparison of the discriminative capabilities of the employed feature models using Principal Component Analysis and the Sammon plot to show the machine condition patterns.