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Recently, word embedding representations have been investigated for slot filling in Spoken Language Understanding, along with the use of Neural Networks as classifiers. Neural Networks, especially Recurrent Neural Networks, that are specifically adapted to sequence labeling problems, have been applied successfully on the popular ATIS database. In this work, we make a comparison of this kind of models with the previously state-of-the-art Conditional Random Fields (CRF) classifier on a more challenging SLU database. We show that, despite efficient word representations used within these Neural Networks, their ability to process sequences is still significantly lower than for CRF, while also having a drawback of higher computational costs, and that the ability of CRF to model output label dependencies is crucial for SLU.