This paper applies artificial neural network ( ANN) to model the observed effluent quality data. The ANN's structure, involving the number of hidden layer and node and their connection, is determined endogenously by resorting to the compromise of data cost minimization and prediction accuracy maximization. To obtain the best compromise possible, the model introduces an aspiration variable ( mu) that represents the level of aspiration achieved in one objective and the conjugate of mu, ( 1-mu), represents level of aspiration achieved in the other objective. Because a massive amount of calculation is required, the model applies genetic algorithm ( GA) for its computational flexibility and capability to ensure global solution. Feasibility and practicality of the model is tested by a case study with a set of 150 daily observations on 17 operational variables and quality parameters at an industrial wastewater treatment plant ( WTP) located in southern Taiwan. Of these 17 variables open to selection, only 6 variables, wastewater flow rate ( Q), CN-, SS, MLSS, pH and COD are selected by the model to achieve the maximum accuracy of prediction, 0.94, with a total cost of 5,950 NT$. By constraining budget availability, the variables included in the model are reduced in number, causing a concomitant reduction in prediction accuracy, that is, by varying mu ( aspiration level of accuracy), a trajectory of cost and accuracy is generated. The calculation results a cost of 3,650 NT$ and 0.54 accuracy for the case with variables including flow rate, SCN- and SS in equalization basin; aeration tank hydraulic retention time ( HRT) and percentage of returned sludge ( R%) are selected for building the prediction model when the importance of required budget is equal to the accuracy of prediction model. In addition, when required cost for building ANN model is between 3,650 NT$ and 3,900 NT$, the marginal return of budget input is highest in the entire range of calculation.