This project will use machine learning approaches to extract physico-chemical information from chemical imaging data. This novel approach will tackle an emerging problem in this field, namely how to automatically identify and extract chemical signals from the rich and ever-larger datasets that it is now possible to collect. There are several features that suggest this problem can be tackled using machine learning approaches. We have developed software for the rapid simulation of chemical imaging data, and we can use this to generate large labelled datasets for training the convolutional neural networks (CNN) that we will build. In addition we have substantial libraries of real data which the developed CNN's can be tested against.