This paper updates our previous work on the automatic phonetic feature analysis of speech. Previously we have described how a bank of feature-detectors can be used as a front-end to traditional speech recognition pattern matching algorithms, with increased performance in speaker-in-dependent isolated word recognition over purely acoustic front-ends. In this paper we extend the feature analysis to continuous speech: describing the labelling methodology and the additional classification performance of neural network classifiers over the Bayes normal classifier.