Condition monitoring and fault diagnosis in modern manufacturing automation is of great practical significance. It improves quality and productivity, and prevents damage to machinery. In general, this practice consists of two parts: 1)extracting appropriate features from sensor signals and 2)recognizing possible faulty patterns from the features. Through introducing the concept of marginal energy in signal processing, a new feature representation is developed in this paper. In order to cope with the complex manufacturing operations, three approaches are proposed to develop a feasible system for online applications. This paper develops intelligent learning algorithms using hidden Markov models and the newly developed support vector techniques to model manufacturing operations. The algorithms have been coded in modular architecture and hierarchical architecture for the recognition of multiple faulty conditions. We define a novel similarity measure criterion for the comparison of signal patterns which will be incorporated into a novel condition monitoring system. The sensor-based intelligent system has been implemented in stamping operations as an example. We demonstrate that the proposed method is substantially more effective than the previous approaches. Its unique features benefit various real-world manufacturing automation engineering, and it has great potential for shop floor applications.