Emission requirements for diesel engines are becoming increasingly strict, leading to the increase of engine architecture complexity. This evolution requires a more systematic approach in the development of control systems than presently adopted, in order to achieve improved performances and reduction of times and costs in design, implementation and calibration. To this end, large efforts have been devoted in recent years to the application of advanced Model-Based MIMO control systems.In the present paper a new MIMO nonlinear feedback control is proposed, based on an innovative data-driven method, which allows to design the control directly from the experimental data acquired on the plant to be controlled. Thus, the proposed control design does not need the intermediate step of a reliable plant model identification, as required by Model-Based methods. In this way, significant advantages over Model-Based methods can be achieved in terms of times and costs in design and deployment as well as in terms of control performances. The method is applied to the control design for the air and charging systems, using experimental data measured on a four cylinder diesel engine with single stage turbocharger. The performances of the designed controller are evaluated on an accurate nonlinear engine model, showing significant reductions of up to 2.7 times for the intake manifold pressure, up to 2.7 times for the oxygen concentration tracking errors and about 4 times in controller design and calibration efforts with respect to a decoupled-gain-scheduled PID controller typically applied for the air charging system control of diesel engines.