Rapid and quantitative analysis of physiological change and clavulanic acid production was studied by the combination of pyrolysis mass spectrometry (PyMS) and artificial neural network (ANN) in Streptomyces clavuligerus. Firstly, the continuous culture studies were carried out to get the physiological background and PyMS samples. Clavulanic acid production was inversely related to growth rate: Mycelium growth and $q_{cal}$ were optimum at 0.1 $h^{-1}; and ;0.025 h^{-1}$ respectively. Changes in specific nutrient uptake rates (<TEx>$q_{gly}$</TEX> and $q_{amn}$) also affected clavulanic acid production since clavulanic acid production appeared to be stimulated by the limitation of carbon and nitrogen. Fermentation broth containing mycelium taken from continuous cultures was analyzed by PyMS, and the PyMS spectra were analyzed with multivariate statistics. PCCV plots revealed that samples harvested under the same culture condition were clustered together but samples from different culture conditions formed separate clusters. To deconvolute the pyrolysis mass spectra so as to obtain quantitative information on the concentration of clavulanic acid, ANN was trained on Py MS data using a radial basis function classifier. The results showed that the physiological stages with different growth rate were successfully differentiated and it was possible to monitor the clavulanic acid production precisely and rapidly.