At ICSLP’98 we presented some preliminary results on automatic preselection list length estimation using parametric and non-parametric techniques, for a flexible large and very large vocabulary, speaker independent, isolated-word hypothesis generation system in a telephone environment, with vocabularies of up to 10000 words. In the baseline system, the preselection module generates a fixed-length list of candidate words, to be given to the verification stage. Our idea is making this length variable, depending on any known-in-advance system parameter, to allow decreasing computational demands. In this paper we present a novel approach to preselection list length estimation. A neural network is used to give an initial estimate of the required length, which is further processed to obtain a final value. The key factor to evaluate different methods is calculating the average preselection list length (effort) while keeping the required error rate.