The water molecules within a single voxel may exist in different microenvironments so that the T2 relaxation is considered as a multi-exponential decay. A few quantitative imaging techniques such as myelin water imaging attempt to extract the short T2 component as a marker specific to myelin. However, decomposition of multi-exponential T2 decay data is an ill-posing problem. Commonly used non-negative least squares fitting method is slow, complex and unstable, even with strong regularization and B1 correction. We used synthetic data to train a single neural network for a better and faster analysis of the multi-exponential T2 decay data.