The efficient production of reducing sugars is an extremely important requirement in the utilization of microalgae as a feedstock in bioethanol production. In this study, for the first time, the time course of reducing sugar production during starch hydrolysis of mixed microalgal biomass under different operational conditions was modeled by two different methods: a-Michaelis-Menten's kinetic model and b-artificial neural network (ANN) method. The results from both models revealed that predicted values are in good agreement with the experimental results. Also, sensitivity analysis indicated that the kinetic model results are less sensitive to K-m and K-i than to V'(max). The applied ANN was a feed-forward back propagation network with Levenberg-Marquardt algorithm. It was found that the order of relative importance of the input variables on reducing sugar concentration predicted by ANN model was as follows: pH>substrate concentration>temperature>hydrolysis time. Subsequently, the results indicated that the maximum reducing sugar yield (96.3%) was achieved by adding enzymes with the sequence of first cellulases, at 50 degrees C, pH 4.5, second -amylase, at 70 degrees C, and pH 6 with a substrate concentration of 50g/L. These findings may be useful for improving the enzymatic hydrolysis of mixed microalgae for bioethanol production.