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Tool condition evaluation and prognosis has been an arduous challenge in the modern semiconductor manufacturing environment. More and more embedded and external sensors are installed to capture the genuine tool status for fault identification. Therefore, tool condition analysis based on real-time equipment data becomes not only promising but also more complex with the rapidly increased number of sensors. In this paper, the idea of generalized moving variance (GMV) is employed to consolidate the pure variations within tool fault detection and classification data into one single indicator. A hierarchical monitoring scheme is developed to generate an overall tool indicator that can coherently be drilled down into the GMVs within functional sensor groups. Therefore, we will be able to classify excursions found in the overall tool condition into sensor groups and make tool fault detection and identification more efficient.