Considerable literature describing the use of artificial neural networks (ANNs) has evolved for a diverse range of applications such as fitting experimental data, machine diagnostics, pattern recognition, quality control, signal processing, process modeling, and process control, all topics of interest to chemists and chemical engineers. Because ANNs are nets of simple functions, they can provide satisfactory empirical models of complex nonlinear processes useful for a wide variety of purposes. This article describes the characteristics of ANNs including their advantages and disadvantages, focuses on two types of neural networks that have proved in our experience to be effective in practical applications, and presents short examples of four specific applications. In the competitive field of modeling, ANN's have secured a niche that now, after two decades, seems secure.