A pipe burst is a major water distribution system failure. Water escapes the network through the break increasing the total flow entering the network. These higher flows, in turn, increase the head losses in pipes and result in lower water pressures at customer taps. This study focuses on burst detection by seeking to identify anomalies in net system demand, pipe flow rates, and nodal pressure heads. Three univariate statistical process control (SPC) methods (the Western Electric Company rules, the cumulative sum (CUSUM) method, and the exponentially weighted moving average [EWMA]) and three multivariate SPC methods (Hotelling T2 method and multivariate versions of CUSUM and EWMA) are compared with respect to their detection effectiveness and efficiency. First, the three univariate methods are tested using real system burst detection and then the six SPC methods are compared using synthetic data. The real application using net system demand shows that burst flows are proportionally too small to be detected. Synthetic data analyses suggest that the univariate EWMA method using nodal pressure provides the highest detectability. The method's long record length helps detect small bursts and avoid false detection. SPC methods require consistent system operations for measurements beyond total area flow.