A significant problem associated with the implementation of Vibration-Based Structural Health Monitoring (VSHM) systems originates from the detrimental effects caused by Environmental and Operational Variations (EOVs). The EOVs cause observations from the same structural condition to behave in different manners. As such, this leads to issues when defining a robust baseline state as well as making the discrimination between undamaged and damaged observations more challenging. In order to address these challenges, multivariate nonlinear regression is implemented to account for the EOVs. Damage Sensitive Features (DSFs) are extracted from acceleration data and then are regressed based on environmental and operational parameters. New features are effectively normalised by finding the difference between the measured and predicted values. This process removes the influence of the EOVs in an explicit manner, allowing for more reliable damage detection. While the benefit of the application of different regression methodologies has already been demonstrated in the past, this work addresses a number of practicalities in the implementation of VSHM on real systems. The analysis investigates the selection of the DSF and is investigated alongside another analysis into how the damage detection behaves under varying amounts of input information. Furthermore, a method is proposed to understand and account for a large number of outliers in the training data.