Process analytical technology (PAT) is a guide to improve process development in biotech industry. Optical sensors such as near and mid infrared spectrometers fulfill an essential part for PAT. NIRS and MIRS were investigated as non-invasive on line monitoring tools for animal cell cultivations in order to predict critical process parameters, like cell parameters as well as substrate and metabolite concentrations. Eight cultivations were performed with frequent sampling. Variances between cultivations were induced by spiking experiments with intent to break correlations between analytes; to keep causality of the models; and to increase model robustness. Calibration models were built for each analyte using partial least-squares regression method. Cultivations chosen for validation were not part of the calibration set. Glucose concentration, cell density and viability were predicted by NIRS with a root mean square error of prediction (RMSEP) of 0.36 g/L, 3.9 10(6)cells/mL and 3.62% respectively. Based on MIR spectra glucose and lactate concentrations were predicted with a RMSEP of 0.16 and 0.14 g/L respectively. Results show that MIRS has higher accuracy regarding the prediction of single analytes. For prediction of a main course of a cultivation, NIRS is much better suited than MIRS.