Machine failure is often an important factor in throughput of manufacturing systems. To simplify the inputs to the simulation model for complex machining and assembly lines, we have derived the Arrows classiﬁcation method to group similar machines, where one model can be used to describe the breakdown times for all of the machines in the group and breakdown times of machines can be represented by ﬁnite mixture model distributions. The Two-Sample Cram´er-von Mises statistic is used to measure the similarity of two sets of data. We evaluate the classiﬁcation procedure by comparing the throughput of a simulation model when run with mixture models ﬁtted to individual machine breakdown times; mixture models ﬁtted to group breakdown times; and raw data. Details of the methods and results of the grouping processes will be presented, and will be demonstrated using an example.