The detection of the onset of damage in gear systems (e.g., gearboxes) is of great importance to a wide array of industries. In this paper, an enhanced diagnostic (ED) system is developed for real-time gear system condition monitoring. A neurofuzzy (NF) paradigm is adopted for pattern classification of the features from the energy, amplitude, and phase domains. The diagnostic reliability is enhanced by properly integrating predicted future machinery states that are forecast by recurrent NF predictors. An online training technique is proposed to improve the classifier's adaptive capability to accommodate different machinery conditions. The viability of this new monitoring system has been verified by experimental tests under different gear conditions. This proposed ED system has also been applied for real-time condition monitoring in multistage printing machines. The primary application has demonstrated its reliability as an effective monitoring tool for both production quality control and maintenance planning.