It is difficult to measure online substrate, biomass, and product concentrations, due to the lack of reliable sensors in the fermentation. In view of this, the pH, dissolved oxygen (DO) concentration, and CO2 production, among others, are usually utilized in bioprocess analysis. With these easily obtained online measurements, it is possible to reconstruct the evolution of the state variables or estimate the bioprocess parameters. Neural networks, which rely on the efficacious nonlinear multivariate analysis capacity and its favorable black-box feature, are most widely applied to bioprocess analysis and fault detection. In this study, an artificial autoassociative neural network (AANN) has been used online to detect deviations from normal antibiotic production fermentation with ordinary state variables. To improve the efficiency of extracting hidden information contained in multidimensional state variables, and finally to render the AANN adequate for fault detection, we have explored the following methods: preprocessing of the data that involved normalizing the training data of the AANN; evaluation of the data that involved assessing the output of the AANN; and selection of state variables. A method for fault detection for virginiamycin production by Streptomyces virginiae was developed.