With the popularity of social media sharing sites such as Flickr or YouTube, tagging has become a more important task to describe the content of the multimedia object. Recently, automatic tagging or tag recommendation has studied to automatically provide a relevant tag to the media by analyzing the user tags. However, the social tags annotated by common users are known to be ambiguous and subjective because of biased tags by individual users. In this paper, we present the task of combining visual content and social tag for tag suggestion. Our method finds the visual neighbors using subject-based visual content analysis, and analyzes the social tags of visual neighbors by weighted neighbor voting technique. This enables to solve the problem that general voting technique can give irrelevant tags caused by the low performance of visual search. We evaluate our method on a social-tagged image database from Flickr by comparing our method to visual-based and tag-based methods. Our experimental results show that our method has an improvement in tag suggestion or image tagging.