The ability to accurately model and simulate chemical processes has been paramount to the growing success and efficiency in process design and operation. These improvements usually come with increasing complexity of the underlying models leading to substantial computational effort in their use. It may also occur that the structure of the model is sometimes unknown making optimization and study difficult. To circumvent these issues, mathematically simpler models, commonly known as surrogate models, have been designed and used to successfully replace these complex, underlying models with much success. This technique has seen increasing use within the chemical process engineering field and this article summarizes some popular surrogates and their recent use in this area.