A common control strategy for the production of recombinant proteins inPichia pastorisusing the alcohol oxidase 1 (AOX1) promotor is to separate the bioprocess into two main phases: biomass generation on glycerol and protein production via methanol induction. This study reports the establishment of a soft sensor for the prediction of biomass concentration that adapts automatically to these distinct phases. A hybrid approach combining mechanistic (carbon balance) and data-driven modeling (multiple linear regression) is used for this purpose. The model parameters are dynamically adapted according to the current process phase using a multilevel phase detection algorithm. This algorithm is based on the online data of CO(2)in the off-gas (absolute value and first derivative) and cumulative base feed. The evaluation of the model resulted in a mean relative prediction error of 5.52% andR(2)of .96 for the entire process. The resulting model was implemented as a soft sensor for the online monitoring of theP. pastorisbioprocess. The soft sensor can be used for quality control and as input to process control systems, for example, for methanol control.