A large body of work exists concerning uncertainty in ocean current measuring high-frequency radar (HFR) systems. This study investigates the magnitude of uncertainty present in a HFR system in the lower Chesapeake Bay region of Virginia. A method of assessing the fundamental performance of the HFR is comparing the radial velocities measured by two facing HF radars at the centre point of their baseline. In an error-free network, radial vectors from the two sites would be equal and opposite at a point on the baseline, so the magnitude of their sum represents a measure of imperfection in the data. Often essential information lies not in any individual process variable but in how the variables change with respect to one another, i.e. how they co-vary. PCA is a data-driven modelling technique that transforms a set of correlated variables into a smaller set of uncorrelated variables while retaining most of the original information. This paper adopts PCA to detect anomalies in data coming from the individual HF stations. A PCA model is developed based on a calibration set of historical data. The model is used with new process data to detect changes in the system by application of PCA in combination with multivariate statistical techniques. Based on a comprehensive analysis the study presents an objective preconditioning methodology for preprocessing of HFR data prior to assimilation into coastal ocean models or other uses sensitive to the divergence of the flow.