Neuronal spike trains display correlations at diverse timescales throughout the nervous system. The functional significance of these correlations is largely unknown, and computational investigations can help us understand their role. In order to generate correlated spike trains with given statistics, several case-specific methods have been described in the litterature. This letter presents two general methods to generate sets of spike trains with given firing rates and pairwise correlation functions, along with efficient simulation algorithms.