An implementation of the recently proposed concept of the Liquid State Machine using a Spiking Neural Network (SNN) is trained to perform isolated word recognition. We investigate two different speech front ends and different ways of coding the inputs into spike trains. The robustness against noise added to the speech is also briefly researched. It turns out that a biologically realistic configuration of the LSM gives the best result, and that it performs very well for the task of speech recognition.