An architecture for temporal classification has been developed which appears to have the potential to overcome some of the disadvantages of existing approaches for temporal classification: the amount of fine tuning required, the amount data required to learn from and the production of meaningful descriptions of the concepts that have been learnt. Preliminary results are promising, though not conclusive. Further investigation of the architecture is definitely warranted and is proceeding.