| Sumario: | Principal component, cluster and discriminant analysis are multivariate statistical methods that are widely used in archaeometry. They are examples of what are known in some literatures as unsupervised and supervised learning methods. Over the past 20 years or so, a wide variety of other learning methods have been developed that take advantage of modern computing power and, in some cases, have been designed to handle data sets more complex than those often used in archaeometric data analysis. To date, these methods have had little impact on archaeometry. This paper reviews, in a largely non-technical manner, the ideas behind these newer methods; illustrates their use on a variety of data sets; and attempts to assess their potential for future archaeometric use.
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