Classical life data analysis typically focuses on a single variable, usually failure time, using statistical distributions such as the Exponential and Weibull distributions, which have been discussed earlier in this book. However, in many applications, life data often involves multiple variables. For example, the lifespan of a car is determined by both age and mileage, the lifespan of an aircraft is influenced by both calendar years and total flying hours. This necessitates the use of multivariate distributions to address such cases. In statistics, a multivariate distribution refers to a method that incorporates more than one variable, making it suitable for analyzing complex relationships between multiple factors. This chapter explores the application of multivariate distributions in reliability analysis. Since multivariate distributions are less familiar in reliability engineering, the chapter begins with an introduction to the basic concepts of Normal multivariate distributions. Later, we will discuss using the Copula function to develop some special multivariate distributions for reliability dependency analysis.