The development of a model for any energy system is required for proper design, operation or its monitoring. Models based on accurate mathematical expressions for physical processes are mostly useful to understand the actual operation of the plant. However, for large systems like combined heat and power (CHP) plants, such models are usually complex in nature. The estimation of output parameters using these physical models is generally time consuming, as these involve many iterative solutions. Moreover, the complete physical model for new equipment may not be available. However, artificial neural network (ANN) models, developed by training the network with data from an existing plant, may be very useful especially for systems for which the full physical model is yet to be developed. Also, such trained ANN models have a fast response with respect to corresponding physical models and are useful for realtime monitoring of the plant. In this paper, the development of an ANN model for the biomass and coal cofired CHP plant of Visthamnsverket at Helsingborg, Sweden has been reported. The feed forward with back propagation ANN model was trained with data from this plant. The developed model is found to quickly predict the performance of the plant with good accuracy. (C) 2007 Elsevier Ltd. All rights reserved.