A direct adaptive neural network-based feedback linearization (NNFBL) slip control scheme for an antilock braking system (ABS) is presented. The NNFBL slip controller is developed to minimise the vehicle braking distance and to simultaneously improve its overall ride comfort and road handling. The comprehensive vehicle model incorporates the passive suspension dynamics, the dynamics of the electro-mechanical based braking system and air drag and wheel bearing friction. A feedforward, multilayer perceptron (MLP) neural network (NN) model that is well suited for control by discrete input-output linearization (NNIOL) is selected to represent the ABS with passive suspension. The NN model was trained using Levenberg-Marquardt optimization algorithm. The controlled signal was further boosted using a genetic algorithm generated gain. The effectiveness of the proposed controller is demonstrated by simulation results, in the presence of deterministic road disturbance input to the suspension and varying road conditions. The results are superior with respect to braking distance minimization and also to reference slip tracking, especially on the dry asphalt road.