Multilingual Neural Machine Translation (NMT) models have yielded large empirical success in transfer learning settings. However, these black-box representations are poorly understood, and their mode of transfer remains elusive. In this work, we attempt to understand massively multilingual NMT representations (with over 100 languages) using Singular Value Canonical Correlation Analysis (SVCCA), a representation similarity framework that allows us to compare representations across different languages, layers and models. Our analysis validates several empirical results and long-standing intuitions, and unveils new observations regarding how representations evolve in a multilingual translation model. We draw two major results from our analysis: (i) Representations of the same sentences across different languages cluster based on linguistic similarity and (ii) Source sentence representations learned by the encoder are dependent on the target language. We further confirm our observations with carefully designed experiments and connect our findings with existing results in multilingual NMT and cross-lingual transfer learning.