The evaluation of the probability that the maximum st-flow in a stochastic flow network fails to meet a predetermined demand is an NP-hard computational problem that calls for smart Monte Carlo simulation. In this talk we describe a Monte Carlo splitting algorithm for the estimation of this probability in highly reliable networks. We show that unlike existing simulation algorithms, the splitting approach can cope with link capacities that have a discrete (countable or uncountable) or continuous joint distribution.