We describe in this paper a speaker independent, global word recognition task using time delay networks. We first describe these networks as a way for learning feature extractors by constrained back-propagation. Such a time-delay network is shown to be capable of dealing with a test task: French digit recognition. The results are discussed and compared, on the same data sets, with those obtained with a classical time warping system. Both connectionist and classical systems achieved no more than 1% errors on the test set.