Risk assessments often focus on extreme events.Inference about extremes faces the problem of iusufficient data with few or no occurrences of the event of interest.Extreme value theory is particularly well suited for handling this type of problems.We use a multivariate extreme value theory approach to establish thresholds for signaling varying levels of extreme in the context of simultaneous monitoring of multiple dependent measurements.The proposed threshold system is well justified in terms of extreme multivariate quantiles, and its sample estimator is shown to be consistent.As an illustration, the proposed approach is applied to developing a threshold system for monitoring airline performances.This threshold system assigns different risk levels to observed airline performances.In particular, it divides the sample space into regions with increasing levels of risk.Moreover, in the univariatc case, such a thresholding technique is used to determine a suitable cut-off point on a runway for holding short of landing aircrafts.This cut-off point is chosen to ensure a certain required level of safety while allowing simultaneous operations on two intersecting runways to ease air traffic congestion.