This paper describes a new criterion of speech recognition using an integrated confidence measure for minimization of the word error rate (WER). Conventional criteria for WER minimization obtain an expected WER of a sentence hypothesis merely by comparing it with other hypotheses in an n-best list. The proposed criterion estimates the expected WER by using an integrated confidence measure with word posterior probabilities for a given acoustic input. The integrated confidence measure, which is implemented as a classifier based on maximum entropy (ME) modeling, is used to get probabilities reflecting whether the word hypotheses are correct or incorrect. The classifier comprises a variety of confidence measures and can deal with a temporal sequence of them in order to attain a more reliable confidence. Our proposed criterion achieved a WER of 7.5% and a 2.6% improvement relative to conventional n-best rescoring methods in transcribing Japanese broadcast news under noisy field and spontaneous speech conditions.