MALDI-Intact Cell Mass Spectrometry (MALDI-ICMS) is a helpful technique for discriminating/identifying microorganisms (MALDI Biotyping); it is used in clinics for rapid pathogen identification in case of hospital-acquired infections (e.g., Staphylococcus aureus), enabling quick response and intervention. Industrial fermentations exploit microorganisms for synthetizing products of interest; while the failure of such fermentations is cost-intensive, process characterization can help decision-making for process optimization. Aiming at developing novel process monitoring tools, we present the use of MALDI-ICMS to monitor E. coli fermentations. Samples from E. coli bioreactors were collected at different fermentation times, aliquots were washed by pelletation and resuspension in a NH4HCO3 buffer (pH 7) and equalized to the same OD550, the cell suspensions were mixed with a MALDI matrix at a ratio of 1:2 (sinapinic acid/ferulic acid, 0.5 % trifluororacetic acid, 70 % acetonitrile). The mixture was spotted in replicates on a MALDI target and air dried. The samples were analyzed on a MALDI-TOF device (UltrafleXtreme, Bruker) in the positive linear mode (2-20 kDa mass range). By using multivariate data analysis in the R environment, we exploited time-dependent spectral changes to build a model capable of monitoring the fermentation progress. While principal component analysis could separate fermentation phases, partial least square regression could track fermentation progress with a prediction error of less than 5 % of the total fermentation time. The presented approach will allow better early-stage process characterization and prediction of process outcome.