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The NVH (Noise Vibration Harshness) behavior of modern vehicles becomes more and more important - especially in terms of new powertrain concepts, like in hybrid electric or full electric vehicles. There are many tools and methods to develop and optimize the NVH behavior of modern vehicles. At the end of the development process, subjective ratings from road tests are very important. For objective analyses, different approaches based on artificial neural networks exist. One example is the AVL-DRIVE™ system, a driveability analysis and benchmarking system which allows, based on a very small set of sensors, an adequate objective rating of the vehicle's driveability. The system automatically detects driving maneuvers and rates the driveability. This article presents a method which is able not only to rate different maneuvers and the behavior of the vehicle but also to detect phenomena and causes in the domain of NVH.