A saturation-type transfer function with a backpropagation neural network (BPNN) was proposed for solving the modeling problem of batch cell growth system. Batch chemical processes are usually influenced by their initial conditions. For batch cell cultures, the initial state strongly governs the growth pattern during the timecourse. In modeling a chemical system, we are always interested in how to model the outcome of the system related to some affecting factors. In a batch system, some of the initial conditions are certainly important affecting factors. Trying to model the cell growth with information concerning only the initial conditions is not yet possible from a kinetic approach. The difficulty comes from numerical analysis and insufficient knowledge regarding certain growth parameters as they vary with time. Accordingly, neural network methodology with the concept developed earlier was proposed to solve this problem. The feasibility and capability of the neural network to model the pattern of batch cell growth by providing initial conditions only is tested in this study. A 2-3-8 BPNN with initial glucose and cell concentrations as the two inputs, cell densities measured at eight each hours as the eight outputs was thus constructed. The simulation and prediction results of this BPNN are presented to demonstrate the performance and applicability of this newly discovered transfer function. Sensitivity analysis of the initial factors from this neural network model (NNM) is also discussed. The optimization of the initial conditions for this system is also performed.