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The paper describes the application of neural networks to understand the links between test drivers' subjective ratings of vehicle handling and measurable vehicle performance metrics as part of the Foresight Vehicle initiative. The shortcomings of classical linear methods used in previous studies (Crolla et al [1, 2 and 3]) are described along with the processes and developments made in a genetic algorithm based methodology used to find the predominantly non-linear links between subjective and objective handling. The techniques used were designed to make allowances for noise and other distractions inherent within drivers' subjective ratings. Further insights into the preferred ranges of the values of important vehicle handling metrics are presented.