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In this paper, a data-driven system based on PCA is described to detect and quantify fluid leaks in an experimental pipeline. A dynamic PCA implementation (DPCA) was used to capture the process dynamics because the system variables are time-correlated. To detect leaks online, the Hotelling’s T2 statistic and the squared prediction error (SPE) were used as residuals, which are compared against statistically defined thresholds from a set of training data. To determine the number of delays to be included in the DPCA model as well as the number of principal components to be used, a tuning process was executed to find the residual with the optimal number of delays and components that showed the best correlation between the residuals and the leakage size. This allowed the construction of a regression model to estimate the flow rate of the leaks directly from the residual.