SI engines are dynamic systems with highly nonlinear characteristics which are controlled by ECUs performing complex control algorithms. Hardware-in-the-Loop (HIL) simulation is an important tool to support test and verification during the development phase. The simulation model has to accurately reflect the dynamic behavior of the SI engine in the whole operating area. This paper describes a neural network approach to identify, i.e. to model a nonlinear dynamic system, the SI engine, represented only by I/O measurement data. The neural models have advantages with respect to robustness and measuring extent. They can be used as stand alone models or as sub-models integrated in a global model based on a physical structure. Measurements from a test bench compared to real-time simulation results prove the performance of the proposed modeling strategy.