Photoelectric factor, formation true resistivity, and formation water saturation are three functional parameters of a hydrocarbon reservoir that could provide invaluable data for reservoir characterization and formation evaluation. The present study proposes an improved strategy for making a quantitative formulation between conventional well log data and the mentionewd parameters. At the first stage of this study, three architectures of artificial neural networks, including generalized regression neural network, radial basis neural network, and Bayesian regulation backpropagation neural network, were employed to predict the aforementioned parameters from conventional well log data. Consequently, a committee neural network was constructed by virtue of hybrid genetic algorithm-pattern search technique. The propounded committee neural network combines the results of generalized regression neural network, radial basis neural network, and Bayesian regulation backpropagation neural network to improve the accuracy of final prediction. It assigns a weight factor to each of the individual artificial neural networks indicating its contribution in overall prediction. A set of data points was used for model construction and another set was employed to assess the model performance. The results showed that integration of artificial neural networks using the concept of committee machine could improve the precision of target prediction, although each of the artificial neural networks has performed adequately for prediction of photoelectric factor and formation true resistivity. The values obtained for formation water saturation are not as accurate as results obtained for photoelectric factor and formation true resistivity, although the correlation coefficient between measured and predicted values for formation water saturation is higher.