The European Watertruck + project introduced a new fleet of self-propelled inland cargo barges to the European waters, in order to induce more sustainable freight transport in the European hinterland. An augmentation of the automation level of this fleet could further advance their competitiveness and potentially pave the way for unmanned inland cargo vessels. The motion control of such a vessel forms a key component in this envisaged automation chain and benefits from the knowledge of the capabilities of the propulsion system, which here envelops a 360-degrees-steerable steering-grid thruster in conjunction with a 360-degrees-steerable four-channel thruster. Therefore, this study details the mechanical design of both thrusters and lists their experimental towing-tank data. Furthermore, two different modelling methods are offered, one theoretically based and one using a multilayer neural network. A model structure comparison, based on a bias-variance trade-off, verifies the adequacy of the theoretical model which got expended with an angle-dependent thrust deduction coefficient. In addition, several multilayer feedforward neural network architectures exemplify their inherent capability to model the complex, nonlinear, flow phenomena inside the thrusters. These identified model structures can additionally improve thrust allocation algorithms and offer better plant models to study more advanced control strategies.