Animal cell cultures are characterized by very complex nonlinear behaviors, difficult to simulate by analytical modeling. Artificial Neural Networks, while being black box models, possess learning and generalizing capacities that could lead to better results. We first trained a three-layer perceptron to simulate the kinetics of five important parameters (biomass, lactate, glucose, glutamine and ammonia concentrations) for a series of CHO K1(Chinese Hamster Ovary, type K1) batch cultures. We then tried to use the same trained model to simulate the behavior of recombinant CHO TF70R. This was achieved, but necessitated to synchronize the time-scales of the two cell lines to compensate for their different specific growth rates.