A prototype Self-Tunning Vision System (STVS) has been developed and used to monitor morphogenetic changes of the yeast-like fungus Aureobasidium pullulans during fermentations for the production of the polysaccharide pullulan. The STVS combines classical image processing techniques, neural networks and fuzzy logic technologies. By combining these technologies the STVS is able to capture the culture image and adapt to new environments. The proposed system can be "tailored" with minimum effort by an expert who "teaches" the system to recognize cells by showing examples of different morphologies. After adaptation, the STVS is able to capture images, isolate the different cells, classify them according to the expert’s criteria and provide the profile of the cell’s population. Classification and analysis of Aureobasidium pullulans were performed by the STVS that enabled to follow changes in population distribution during the fermentation, and pointed to the most productive morphogenetic stage of the microorganism during the fermentation. The STVS led to a better understanding of the linkage between the morphogenesis of the yeast-like fungus and the production of the biopolymer at various scale of the fermentation.