Artificial neural networks (ANNs), sometimes referred to as computational neural networks (CNNs), are an attempt to emulate features of biological neural networks in order to address a range of difficult information processing, analysis and modeling problems. Section 8.3.1, Introduction to artificial neural networks, includes several examples of the application of ANN to practical problems in spatial analysis, including land-use classification and spatial interaction modeling. Within the field of spatial analysis only certain types of ANNs have been found to be especially useful to date. This is not to say that other forms of ANN, developed within the computational science field, are not applicable to such problems, but rather that these areas have yet to be explored in depth. There is also considerable ongoing debate as to whether such methods simply amount to data fitting, with little or no value beyond that of description, an issue that will become clearer as we discuss the issue of generalization of such methods to unseen datasets.