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A reliable monitoring system is critically needed in a wide range of industries to detect the occurrence of a fault to prevent machinery performance degradation, malfunction, and sudden failure. In this paper, a new intelligent system, extended neurofuzzy (ENF) scheme, is proposed for real-time machinery condition monitoring. The monitoring reliability is improved by integrating the predicted machinery condition to fault diagnosis. The ENF scheme can perform both classification and prediction operations. The ENF classifier integrates the merits of several signal processing techniques for a more positive assessment of the machinery condition. The ENF predictor forecasts the machinery condition propagation trends. An interscheme training technique is proposed to improve the ENF system's adaptive capability to accommodate different operation conditions. The viability of this new monitoring system has been verified by experimental tests. Test results have shown that the developed ENF system is a robust condition monitoring tool that has good adaptive capabilities to accommodate different machinery conditions.