The Time-Slicing paradigm is a newly developed method for the training of neural networks for speech recognition. The neural net is trained to spot the syllables in a continuous stream of speech. It generates a transcription of the utterance, be it a word, a phrase, etc. Combined with a simple error recovery method the desired units (words or phrases) can be retrieved. This paradigm uses a recurrent neural network trained in a modular fashion with natural connectionist glue. It processes the input signal sequentially regardless of the input's length and immediately extracts the syllables spotted in the speech stream. As an example, this character string is then compared to a set of possible words, picking out the five closest candidates. In this paper we describe the time-slicing paradigm and the training of the recurrent neural network together with details about the training samples. It also introduces the concept of natural connectionist glue and the recurrent neural network's architecture used for this purpose. Additionally we explain the errors found in the output and the process to reduce them and recover the correct words. The recognition rates of the network and the recovery rates for the words are also shown. The presented examples and recognition rates demonstrate the potential of the time-slicing method for continuous speech recognition.