Two levels of control are crucial to the robustness of an artificial beta-cell, a medical device that would automatically regulate blood glucose levels in patients with type I diabetes. A low-level component would attempt to regulate blood glucose continuously, whereas a supervisory-level, or monitoring, component would detect underlying changes in the subject's glucose insulin dynamics and take corrective actions accordingly. These underlying changes, or "faults", can include changes in insulin sensitivity, sensor problems, and insulin delivery problems, to name a few. A multivariate statistical monitoring technique, principal component analysis (PCA), has been applied to both simulated and experimental type I diabetes data. The objective of this study was to determine if PCA could be used to distinguish between normal patient data and data for abnormal conditions that included a variety of "faults." The PCA results showed a high degree of accuracy; for data from nine type I diabetes subjects under ambulatory conditions, 33 of 37 total test days (89%), including fault days and normal days, were classified correctly. Therefore, the proposed monitoring technique shows considerable promise for incorporation into an artificial beta-cell.