Currently, vibration-based damage detection is an area of signi"cant research activity. This paper attempts to extend the research in this "eld through the application of statistical analysis procedures to the vibration-based damage detection problem. The damage detection process is cast in the context of a statistical pattern recognition paradigm. In particular, this paper focuses on applying statistical process control methods referred to as &control charts' to vibration-based damage detection. First, an autoregressive (AR) model is "t to the measured acceleration}time histories from an undamaged structure. Residual errors, which quantify the di!erence between the prediction from the AR model and the actual measured time history at each time interval, are used as the damage-sensitive features. Next, the X-bar and S control charts are employed to monitor the mean and variance of the selected features. Control limits for the control charts are constructed based on the features obtained from the initial intact structure. The residual errors computed from the previous AR model and subsequent new data are then monitored relative to the control limits. A statistically signi"cant number of error terms outside the control limits indicate a system transit from a healthy state to a damage state. For demonstration, this statistical process control is applied to vibration test data acquired from a concrete bridge column as the column is progressively damaged.