The Internet is experiencing an explosion of information presented in different languages. Though written in different languages, some articles implicitly share common concepts. In this paper, we propose a novel framework to mine cross-language common concepts from unaligned web documents. Specifically, visual words of images are used to bridge articles in different languages and then common concepts of multiple languages are learned by using an existing topic modeling algorithm. We conduct cross-lingual text classification in a real-world data set using the mined multilingual concepts from our method. The experiment results show that our approach is effective to mine cross-lingual common concepts.