Lipase is an industrially important enzyme with major use in food industries. The demand of lipase is increasing every year. An online prediction of cell mass concentration is of great value in real time process involving the production of lipase. In the current work, the use of a back-propagation multilayer neural network to predict cell mass during lipase production by Rhizopus delemar NRRL 1472 is targeted. Network training data with respect to time is generated by carrying out experiments in laboratory. The fungus is grown in erlenmeyer flasks at initial pH of 5.6, temperature of 30ºC, and at 150 rpm. During the experiments, readings for cell mass growth are collected in specific period of time. By the training data, an artificial neural network model programmed in MATLAB for Windows is trained and used for prediction of cell mass. The Levenberg-Marquardt algorithm with back-propagation is used in the network to get the optimized weights. The optimum network configuration with different activation function and the number of nodes in the hidden layer are identified by trial and error method. Sigmoid unipolar activation function is 2-5-1, whereas logarithmoid and sigmoid bipolar is 2-3-1. These are chosen according to the values of Sum of Square of Errors (SSE), Root Mean Square (RMS) training and testing. The sigmoid unipolar activation function gives a good fit for estimated value with network configuration 2-5-1, which could be used for generalization.