This paper discusses an integrated spoken language understanding method using a statistical translation model from words to semantic concepts. The translation model is an N-gram-based model that can easily be integrated with speech recognition. It can be trained using annotated corpora where only sentence-level alignments between word sequences and concept sets are available, by automatic alignment based on cooccurrence between words and concepts. It can reduce the effort for explicitly aligning words to the corresponding concept. The method determines the confidence of understanding hypotheses for rejection in a similar manner to word-posterior-based confidence scoring in speech recognition. Experimental results show the advantages of integration over a cascaded method of speech recognition and word-to-concept translation in spoken language understanding with confidence-based rejection.