This study is conducted in the context of developmental learning in embodied agents who have multiple data sources (sensors) at their disposal. We describe an online learning method that simultaneously discovers "meaningful" concepts in the associated processing streams, extending methods such as PCA, SOM or sparse coding to the multimodal case. In addition to the avoidance of redundancies in the concepts derived from single modalities, we claim that "meaningful" concepts are those who have statistical relations across modalities. This is a reasonable claim because measurements by different sensors often have common cause in the external world and therefore carry correlated information. To capture such cross-modal relations while avoiding redundancy of concepts, we propose a set of interacting self-organization processes which are modulated by local predictability. To validate the fundamental applicability of the method, we conduct a plausible simulation experiment with synthetic data and find that those concepts which are predictable from other modalities successively "grow", i.e., become overrepresented, whereas concepts that are not predictable become systematically under-represented. We conclude the article by a discussion of applicability in real-world robotics scenarios.