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This thesis focuses on the development of data-driven automated techniques to enhance performance assessment methods. These techniques include process control loop status monitoring, fault localisation in a number of interacting control loops and the detection and isolation of multiple oscillations in a multi-loop situation. Not only do they make use of controlled variables, but they also make use of controller outputs, indicator readings, set-points and controller settings. The idea behind loop status is that knowledge of the current behaviour of a loop is important when assessing MVC-based performance, because of the assumptions that are made in the assessment. Current behaviour is defined in terms of the kind of deterministic trend that is present in the loop at the time of assessment. When the status is other than steady, MVC-based approaches are inappropriate. Either the assessment must be delayed until steady conditions are attained or other methods must be applied. When the status is other than steady, knowledge of current behaviour can help identify the possible cause. One way of doing this is to derive another statistic, the overall loop performance index (OLPI), from loop status. The thesis describes a novel fault localisation technique, which analyses this statistic to find the source of a plant-wide disturbance, when a number of interacting control loops are perturbed by a single dominant disturbance/fault. Although the technique can isolate a single dominant oscillation, it is not able to isolate the sources of multiple, dominant oscillations. To do this, a novel technique is proposed that is based on the application of spectral independent component analysis (ICA). Spectral independent component analysis (spectral ICA) is based on the analysis of spectra derived via a discrete Fourier transform from time domain process data. The analysis is able to extract dominant spectrum-like independent components each of which has a narrow-bank peak that captures the behaviour of one of the oscillation sources. It is shown that the extraction of independent components with single spectral peaks can be guaranteed by an ICA algorithm that maximises the kurtosis of the independent components (ICs). This is a significant advantage over spectral principle component analysis (PCA), because multiple spectral peaks could be present in the extracted principle components (PCs), and the interpretation of detection and isolation of oscillation disturbances based on spectral PCs is not straightforward. The novel spectral ICA method is applied to a simulated data set and to real plant data obtained from an industrial chemical plant. Results demonstrate its ability to detect and isolate multiple dominant oscillations in different frequency ranges.