Tool condition prognosis has been an arduous challenge in modern semiconductor manufacturing environment, especially for the foundry and analog companies with high product-mix and complicated technology nodes. More and more embedded and external sensors are installed to capture the genuine tool status for tool fault detection and identification (FDC) and thus, tool condition analysis based on the real-time equipment data becomes promising but also much more complex with this rapidly-increased number of sensors. In this research, the generalized moving variance (GMV) technique is firstly validated to demonstrate that the consolidation of the pure variations within tool FDC data into one indicator is feasible. Based on GMV, a hierarchical tool condition monitor scheme is developed by analyzing the GMV's within functional clusters of sensors. With the introduction of this hierarchy, abnormal tool condition can be diagnosed and drilled down into sensor level for an efficient root cause analysis.