In conclusion, as it is structured now, the hybrid strategy we present here is a sophisticated first level QC strategy that can be applied to both real-time and delayed-mode data but that still requires a more in-depth analysis in a second instance which will still require an expert. Future work should include a more structured involvement of experts in the automated test inputs, i.e. in the setting of flag thresholds and in training the fuzzy system to incorporate local knowledge. Further, modelling the sensor and sensor platform (arrays of sensors observing different phenomena) behaviour by using methods that provide probability distributions for sets of possible sensor readings (given the current model state) will contribute significantly in advancing the system. Bayesian quality control techniques which incorporate observed system behaviour and expert belief of physical phenomena (Smith et al., 2012) and time warping pattern matching and function curve fitting techniques (Shahriar et al., 2011) are potential approaches which can be explored. Pattern matching systems can be used to find similar patterns to a slice of the current time series in the historical dataset in order to forecast what is likely to occur in the near future (Shahriar et al., 2011).