A growing literature within the field of chemical engineering describing the use of artificial neural networks (ANN) has evolved for a diverse range of engineering applications such as fault detection signal processing, process modeling, and control. Because ANN are nets of basis functions, they can provide good empirical models of complex nonlinear processes useful for a wide variety of purposes. This article describes certain types of neural networks that have proved to be effective in practical applications, mentions the advantages and disadvantage of using them, and presents four detailed chemical engineering applications. In the competitive field of modeling, ANN have secured a niche that now, after one decade, seems secure.