For an adequate control of the reductant flow in selective catalytic reduction of NOx in diesel exhaust, a tool has to be available to accurately and quickly predict the engine＇s NOx emission. For these purposes, elaborate computer models and expensive NOx analyzers are not feasible. The application of a neural network is proposed instead. Measurements were performed on a transient operating diesel engine, One part of the data was used to train the network for NOx emission prediction, the other part was used to test, The average absolute deviation between the predicted and measured NOx emission is 6.7 %, The reductant buffering capacity of the deNOx catalyst will diminish the effect of the deviation on the overall NOx removal efficiency. The high accuracy of the neural network predictions, combined with the short computation times (0.2 ms/data point), makes the neural network a very promising tool in automotive NOx control.