A new modelling and classification approach for human gait evaluation is proposed. The body movements are obtained using a sensor suit recording inertial signals that are subsequently modelled on a humanoid frame with 23 degrees of freedom (DOF). Measured signals include position, velocity, acceleration, orientation, angular velocity and angular acceleration. Using the features extracted from the sensory signals, a system with induced symbolic classification models, such as decision trees or rule sets, based on a range of several concurrent features has been used to classify deviations from normal gait. It is anticipated that this approach will enable the evaluation of various behaviours including departures from the normal pattern of expected behaviour. The approach is described and the characteristics of the algorithm are presented. The results obtained so far are reported and conclusions are drawn.