An artificial neural network was used to model the chemical interaction between fly ash and lime with different ratios in water-cured compacts. As inputs for the model, different process parameters like pozzolanicity, surface area, unburnt carbon content of the ash samples, water-curing periods, and the proportion of initial lime content in the fly ash-lime mixes were used. Free lime remaining in the compacts after different curing periods was used as the output parameter. A generalized feedforward back-propagation three-layered neural network model was used with a tanhyperbolic transfer function at both the input and the output layer with 400 exemplars. For the training data, after 3500 iterations, the error value was found to be the minimum for the prediction mode. When the model was tested for the test data, the difference between the actual value of the strength and the predicted value of the strength was found to be within +/- 15%.