Abstract: In many real-life applications, it is impossible or unnecessary to distinguish individual objects or elements in universe of discourses. With the view point of granular computing, the notion of a granule may be interpreted as one of the numerous small particles forming a larger unit. Human beings often observe objects or deal with data hierarchically structured at different levels of granulations. There are different granules at different levels of scale in data sets having hierarchical structures. These different granulations often use different labels to mark. The purpose of this study is to analyze representations of granules and uncertainty measures in multi-label ordered information systems. The concept of multi-label information systems is first reviewed. The notion of granular information transformation functions between ordered sets is redefined in multi-label information systems. These functions are ordered preserving. Then, the concept of multi-label ordered information systems is introduced. By employing the new functions of granular information transformation, hierarchical structures of granules with different levels of granulations are derived in multi-label ordered information systems. At each level of granulations, dominance classes and dominated classes determined by dominance relations are also produced. Lower and upper approximations of any subset of the universe of discourse constructed by both dominance classes and dominated classes in multi-label ordered information systems are further explored, and their properties are examined. Finally, uncertainty measures such as roughness, information entropy and rough entropy of an object set at each level of granulations are also presented as usual way in multi-label ordered information systems. Monotonic properties of these uncertainty measures with different levels of granulations are analyzed.