The aim of the project is to reliably identify the set of constructive features responsible for the highest noise levels in the interior of motor vehicles. A simulation environment based on artificial intelligence techniques such as neural networks and genetic algorithms has been implemented. We used a system identification approach in order to approximate the functional relationship between the target noise series and the sets of constructive parameters corresponding to the cars. The noise levels were measured with a microphone positioned on the driver''s chair, and corresponded to a variation of the engine rotation of 600-900 rot/min. The database includes 45 different cars, each described by vectors of 67 constructive features. The available (rotation, noise level) data series were compactly described by means of a reduced set of characteristic features obtained by computing the Discrete Wavelet Transform (DWT) and keeping only the most significant coefficients, which act as the true target series to be modeled. A neural network of RBF type was used to model the relationship between the constructive parameters vectors and the DWT coefficients. The training procedure is based on the Genetic Algorithms (GA) approach. An accurate input-output model was obtained, enabling the interpretation of the found dependencies. This was accomplished through sensitivity analysis, which measures the effect of including or eliminating each constructive parameter on the perceived noise level.