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Once regarded as an eccentric and unpromising algorithm for the analysis of scientific data, the artificial neural network (ANN) has been developed into a powerful computational tool. Compared to a traditional regression approach, with its excellent fault tolerance, the ANN is capable of modeling complex nonlinear relationships and is highly scalable with parallel processing. So its use now spans all areas of science, from the physical sciences and processing to the life sciences and allied subjects. When the data explosion in modern food processing research requires sophisticated analysis methods to uncover the hidden causal relationships between single or multiple responses and a large set of properties, the ANN is one of the most versatile tools to meet the demand. Therefore, the main ANN architectures are described briefly in this review and examples of their application to solve food processing problems are presented as well. Finally, it is suggested that different architectures of ANN and learning algorithms should be introduced into food processing, and the possibility of implementing a neural network based class-modeling algorithm should be studied as well.