We study the use of morphosyntactic knowledge to process N-best lists. We propose a new score function that combines the parts of speech (POS), language model, and acoustic scores at the sentence level. Experimental results, obtained for French broadcast news transcription, show a significant improvement of the word error rate with various decoding criteria commonly used in speech recognition. Interestingly, we observed more grammatical transcriptions, which translates into a better sentence error rate. Finally, we show that POS knowledge also improves posterior based confidence measures.