The Local Area Augmentation System (LAAS) is a ground-based differential GPS system being developed to support aircraft precision approach and landing navigation with guaranteed integrity. Stanford University has designed, implemented and tested a LAAS ground Facility (LGF) prototype, known as the Integrity Monitor Testbed (IMT), which is used to insure that the LGF meets its requirements for navigation integrity. One significant integrity risk is that the mean of the pseudorange correction error distribution becomes non- zero or that its standard deviation (sigma) grows to exceed the broadcast correction error sigma ( ópr_gnd) during LAAS operation. Real-time mean and sigma monitoring is necessary to help insure that the true error distribution is bounded by a zero-mean Gaussian distribution with the broadcast sigma value. In addition to ensuring that the error distribution based on the broadcast sigmas overbounds the true error distribution under nominal conditions, mean and sigma monitoring is needed to detect violations due to unexpected anomalies with acceptable residual integrity risk. Both mean/sigma estimation and Cumulative Sum (CUSUM) methods are useful in this respect. For sigma monitoring, estimation more rapidly detects small violations of ópr_gnd, but the “fast-impulse-response” (FIR) CUSUM variant more promptly detects significant violations that would pose a larger threat to user integrity. Based on these analytical results, mean and sigma estimation and CUSUM methods have been implemented in the IMT and have been tested under both nominal and failure conditions. Under nominal conditions, both sigma estimates and CUSUMs stay below the relevant detection thresholds for all visible satellites in the IMT datasets we have tested. In failure testing, both sigma estimation and CUSUM methods reliably detect injected sigma violations, although both methods are limited by the 200- second interval between independent B-values. Similar results were obtained in testing of the mean monitors. Overall, both methods work smoothly and predictably for sigma and mean monitoring to maintain user integrity under both nominal and failure conditions.