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A probabilistic answer selection for a spoken dialog system based on Conditional Random Fields (CRFs) is described. The probabilities of answers for a question is trained by CRFs based on the lexical and morphological properties of each word, the most likely answer against the recognized word sequence of question utterance will be chosen as the system output. Various set of feature functions were evaluated on the real data of a speech oriented information kiosk system, and it is shown that the morphological properties introduces positive effects on the response accuracy. Training with recognizer output of training database instead of manual transcription was also investigated. It was also shown that this proposed scheme can achieve higher accuracy than a conventional keyword-based answer selection.