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In the work presented in this paper Statistical Process Control (SPC) techniques are applied to a model-based Fault Detection and Isolation (FDI) approach. The residuals, produced as outputs from the FDI system, are manipulated with typical SPC charts to improve the overall diagnosis process. The charts explained in this work: Shewhart control chart, Cumulative Sum (CUSUM) control chart and Exponentially Weighted Moving Average (EWMA) charts are able to accurately determine significant deviations in the residuals. The integration of model-based tools with SPC supervision can be a step towards robustness and effectiveness in fault detection. This scheme reduces the number of false alarms, which is an important aspect in FDI tasks, and can reduce the fault isolation time. This approach has been applied to a laboratory plant with real data, obtaining interesting results.