Simple, linear classical controllers are highly popular in industrial applications. However, most controllers have to be tuned and manually re-tuned on a trial and error basis at every operating level. This is particularly difficult when the plant to be controlled is significantly nonlinear. The deficiency in localised linearised models associated with `local model networks' has been overcome by the introduction of `linear approximation model (LAM) networks'. To address this problem and help in the design of industrial controllers for a wider range of operating trajectories,y, this paper develops a controller network design technique based upon a LAM network of a practical or nonlinear system to be controlled. This is called a `Trajectory Controller Network (TCN)', which overcomes the deficiency associated with local controller networks. Each element of a TCN can be of a simple form, such as PID, and may be obtained directly from a set of step response data at several typical operating levels for fast prototyping. Since plant step response data are often readily available in control engineering practice, such TCNs can be automatically and optimally evolved from these data directly without the need for model identification. The overall controller is co-ordinated and evolved along the entire operating trajectory in the operating envelope, tackling the control problem of practical or nonlinear plants. Evolutionary computation provides global structural search for the network and multi-objective optimisation of the controllers. This novel technique is illustrated and validated through a nonlinear control example.