This paper presents the use of inverse neural networks (INN) for temperature control of a biochemical reactor and its effect on ethanol production. The process model is derived indicating the relationship between temperature, pH and dissolved oxygen. Using fundamental model obtained data sets; an inverse neural network has been trained using the back-propagation learning algorithm. Two types of temperature profile are used to compare the performance of the INN and conventional PID controllers. These controllers have been simulated in MATLAB for a quantitative comparison. The results obtained by the neural network based INN controller and by the PID controller are presented and compared. There is an improvement in the performance of INN controller in settling time and dead time and steady state error over the PID controller. (C) 2013 Elsevier Ltd. All rights reserved.