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. In terms of effort, one main requirement was to use the same sensor set as the driveability evaluation system and no additional equipment.Basis for the method is a large database consisting of about 104 NVH phenomena. In this database the causes and phenomena are linked to concrete driving maneuvers. That is permissible because most phenomena can only occur within one specific maneuver. One example is the so-called CLONK (powertrain phenomenon), which only appears during a load change. The phenomena are identified by using patterns of characteristic frequency ranges. So the user automatically gets the rating on the one hand and information about possible causes on the other.This method will be illustrated by the examples of the humming of the climatic compressor and the coolant pump as well as the vibrations during the restarting of the combustion engine in a hybrid test vehicle. Basis for the validation of this method is data from experiments on the track and the acoustic roller test bench at the IPEK - Institute of Product Engineering Karlsruhe.