A set of road traffic pass-by noises containing more than 2000 vehicles was recorded following the Statistical Pass-By (SPB) methodology. Besides the acoustic descriptors, psychoacoustic indicators (loudness, sharpness, roughness, fluctuation strength) were retrieved for each pass-by of three vehicle categories defined in the standard (passenger cars, dual-axles and multi-axles heavy vehicles). A fourth vehicle category, comprised of delivery vans, was also investigated. All psychoacoustic indicators significantly differed among vehicle categories, meaning that not only intensity descriptors but also temporal and spectral features of pass-by noise distinguish those classes. With enough instances and a balanced dataset across groups, a machine-learning classification algorithm was trained with 70% of the dataset to predict vehicle categories using the psychoacoustic indicators. Classification accuracy on the test set reached 72%. Accuracy losses were primarily caused by 25% of the actual passenger cars being misclassified as vans and vice-versa. These results show the potential of using noise features other than uniquely the maximum noise level to classify vehicles in terms of noise perception. In this way, limiting classifications based on visual aspects of vehicle categories may be overcome, increasing the practicality and accuracy of measurements such as the SPB, as vehicle fleets worldwide are more consistently represented.