Semantic role labeling (SRL) identifies the predicate-argument structure in text with semantic labels. It plays a key role in understanding natural language. In this paper, we present POLYGLOT, a multilingual semantic role labeling system capable of semantically parsing sentences in 9 different languages from 4 different language groups. The core of POLYGLOT are SRL models for individual languages trained with automatically generated Proposition Banks (Akbik et al., 2015). The key feature of the system is that it treats the semantic labels of the English Proposition Bank as "universal semantic labels": Given a sentence in any of the supported languages, POLYGLOT applies the corresponding SRL and predicts English Prop-Bank frame and role annotation. The results are then visualized to facilitate the understanding of multilingual SRL with this unified semantic representation.