Engine Electronic Control (EEC) systems on spark ignition engines enable a high degree of performance optimisation to be achieved through strategy and calibration details in software, but development times and costs can be high. The range of functions performed by EEC systems, and the level of performance demanded, are increasing and new methods of development are required. In the paper, the use of neural networks in the development and implementation of open-loop control of air/fuel ratio during engine transient operating conditions is described. The investigation has addressed the definition of suitable networks, the procedure and data required to train these, and assessment of real-time performance of the implemented system. The potential benefits of the approach include reduced calibration effort and simplification of the control strategy. The results presented demonstrate that a small number of test data are sufficient to define the transient compensation requirements for the network. Errors in the definition of the training data are reduced by an iterative redefinition and training procedure. Three iterations have proved to be the optimum, with additional iterations failing to improve performance. The control of air/fuel ratio to within one unit is achieved at almost all of the transient test cases evaluated.