In this paper, we use the framework of neural machine translation to learn joint sentence representations across different languages. Our hope is that a representation which is independent of the language a sentence is written in, is likely to capture the underlying semantics. We search and compare more than 1.4M sentence representations in three different languages and study the characteristics of close sentences. We provide experimental evidence that sentences that are close in embedding space are indeed semantically highly related, but often have quite different structure and syntax. These relations also hold when comparing sentences in different languages.