Traditional survival analysis was developed to investigate both the occurrence and the timing of an event, but researchers have recently begun to ask questions about the order and timing of multiple events. A multiple event process survival mixture model is developed here to analyze non-repeatable events measured in discrete-time that are not mutually exclusive. The model assumes the population is composed of a finite number of subpopulations of individuals who are homogeneous with respect to the risk of multiple events over time, in order to parsimoniously describe the underlying multivariate distribution of hazard functions. The model builds on both traditional univariate survival analysis and univariate survival mixture analysis. The model is applied to two empirical data sets, one concerning transitions to adulthood and another concerning age of first use of a number of substances. Promising opportunities, as well as possible limitations and future directions are discussed.