In most statistical machine translation (SMT) systems, bilingual segments are extracted via word alignment. In this paper we compare alignments tuned directly according to alignment F-score and BLEU score in order to investigate the alignment characteristics that are helpful in translation. We report results for two different SMT systems (a phrase-based and an n-gram-based system) on Chinese to English IWSLT data, and Spanish to English European Parliament data. We give alignment hints to improve BLEU score, depending on the SMT system used and the type of corpus.