We consider counting process methods for analysing time-to-event data with multiple or recurrent outcomes, using the models developed by Anderson and Gill, Wei, Lin and Weissfeld and Prentice, Williams and Peterson. We compare the methods, and show how to implement them using popular statistical software programs. By analysing three data sets, we illustrate the strengths and pitfalls of each method. The first example is simulated and involves the effect of a hidden covariate. The second is based on a trial of gamma interferon, and behaves remarkably like the first. The third and most interesting example involves both multiple events and discontinuous intervals at risk, and the three approaches give dissimilar answers. We recommend the AG and marginal models for the analysis of this type of data.