Near Infrared (NIR) spectroscopy was used to in situ monitoring the cultivation of two recombinant Saccharomyces cerevisiae strains producing heterologous cyprosin B. NIR spectroscopy is a fast and non-destructive technique, that by being based on overtones and combinations of molecular vibrations requires chemometrics tools, such as partial least squares (PLS) regression models, to extract quantitative information concerning the variables of interest from the spectral data. In the present work, good PLS calibration models based on specific regions of the NIR spectral data were built for estimating the critical variables of the cyprosin production process: biomass concentration, cyprosin activity, cyprosin specific activity, the carbon sources glucose and galactose concentration and the by-products acetic acid and ethanol concentration. The PLS models developed are valid for both recombinant S. cerevisiae strains, presenting distinct cyprosin production capacities, and therefore can be used, not only for the real-time control of both processes, but also in optimization protocols. The PLS model for biomass yielded a R(2) =0.98 and a RMSEP=0.46g dcw.l(-1), representing an error of 4% for a calibration range between 0.44 and13.75g dcw.l(-1). A R(2)=0.94 and a RMSEP=167 U.ml(-1) were obtained for the cyprosin activity, corresponding to an error of 6.7% of the experimental data range (0-2509 U.ml(-1)), whereas a R(2)=0.93 and RMSEP=672 U.mg(-1) were obtained for the cyprosin specific activity, corresponding to an error of 7% of the experimental data range (0-11,690 U.mg(-1)). For the carbon sources glucose and galactose, a R(2)=0.96 and a RMSECV of 1.26 and 0.55g.l(-1), respectively, were obtained, showing high predictive capabilities within the range of 0-20g.l(-1). For the metabolites resulting from the cell growth, the PLS model for acetate was characterized by a R(2)=0.92 and a RMSEP=0.06g.l(-1), which corresponds to a 6.1% error within the range of 0.41-1.23g.l(-1); for the ethanol, a high accuracy PLS model with a R(2)=0.97 and a RMSEP=1.08g.l(-1) was obtained, representing an error of 9% within the range of 0.18-21.76g.l(-1). The present study shows that it is possible the in situ monitoring and prediction of the critical variables of the recombinant cyprosin B production process by NIR spectroscopy, which can be applied in process control in real-time and in optimization protocols. From the above, NIR spectroscopy appears as a valuable analytical tool for online monitoring of cultivation processes, in a fast, accurate and reproducible operation mode.