The CHiME challenge series has been aiming to advance the development of robust automatic speech recognition for use in everyday environments by encouraging research at the interface of signal processing and statistical modelling. The series has been running since 2011 and is now entering its 4th iteration. This chapter provides an overview of the CHiME series including a description of the datasets that have been collected and the tasks that have been defined for each edition. In particular the chapter describes novel approaches that have been developed for producing simulated data for system training and evaluation, and conclusions about the validity of using simulated data for robust speech recognition development. We also provide a brief overview of the systems and specific techniques that have proved successful for each task. These systems have demonstrated the remarkable robustness that can be achieved through a combination of training data simulation and multicondition training, well-engineered multichannel enhancement and state-of-the-art discriminative acoustic and language modelling techniques.