This paper describes a method to detect smiles and laughter sounds from the video of natural dialogue. A smile is the most common facial expression observed in a dialogue. Detecting a user's smiles and laughter sounds can be useful for estimating the mental state of the user of a spoken-dialogue-based user interface. In addition, detecting laughter sound can be utilized to prevent the speech recognizer from wrongly recognizing the laughter sound as meaningful words. In this paper, a method to detect smile expression and laughter sound robustly by combining an image-based facial expression recognition method and an audio-based laughter sound recognition method. The image-based method uses a feature vector based on feature point detection from face images. The method could detect smile faces by more than 80% recall and precision rate. A method to combine a GMM-based laughter sound recognizer and the image-based method could improve the accuracy of detection of laughter sounds compared with methods that use image or sound only. As a result, more than 70% recall and precision rate of laughter sound detection was obtained from the natural conversation videos.