Artificial Neural Networks (ANN) are effective in modeling of non-linear multi variable relationships and also referred to as black box models. Generally, for modeling of heat exchangers the various parameters to be taken into account are inlet and Outlet temperatures Of Shell, tube side fluids. and their flow rates. In the present paper, the concentration of flowing fluids is also considered as one of the variable parameters for heat exchanger modeling. For the study three different fluids are used, (i) water, (ii) 20% glycerin and (iii) 40% glycerin. Heat exchanger model is developed using optimized 2 ANN architecture(1). ANN model is trained using a water-water and water-40% glycerin(3) system. The trained networks are then used for prediction of shelf and tube side exit temperatures for water-20% glycerin 3 system. It is observed that predicted values of water-20% glycerin system are in close agreement (98-99%) with the actual values.