Robust and efficient modeling of industrial processes is vital in realizing stable and economical process design, operation, and control. However, inherent uncertainty of the processes makes the modeling very challenging. Uncertainty is of three dimensions: location, level, and nature. The location of uncertainty identifies a source of uncertainty, the level of uncertainty places the source of uncertainty within the range between absolute determinism and total ignorance, and the nature of uncertainty classifies the source of uncertainty into reducible or non-reducible uncertainty. This study focuses on identification of these dimensions of uncertainty in process modeling. In this regard, models of a steelmaking process and a naphtha reforming process are taken as case studies, and all the three dimensions of uncertainty are elaborated with relevant examples from the two process models. Also, methods of uncertainty analysis are discussed. This review aims to provide a platform for model developers to identify the sources of uncertainty and efficiently model its effect on decision making in process design, operation, and control.