Entropy Guided Transformation Learning (ETL) is a machine learning strategy that extends Transformation Based Learning by providing automatic template generation. In this work, we propose an ETL approach to the clause identification task. We use the English language corpus of the CoNLL'2001 shared task. The achieved performance is not competitive yet, since the F _β=1 of the ETL based system is 80:55, whereas the state-of-the-art system performance is 85:03. Nevertheless, our modeling strategy is very simple, when compared to the state-of-the-art approaches. These first findings indicate that the ETL approach is a promising one for this task. One can enhance its performance by incorporating problem specific knowledge. Additional features can be easily introduced in the ETL model.