Numerical classification encompasses a variety of techniques for the grouping of entities based on the resemblance of their attributes according to mathematically stated criteria. In ecology this usually involves classification of collections representing sites or sampling periods, or classification of species. Classification can thus simplify patterns of collection resemblance or species distribution patterns in an instructive and efficient manner. Procedures of numerical clssification are thoroughly reviewed, including data manipulations, computation of resemblance measures and clustering methods. Agglomerative clustering methods which distort spatial relationships and intensely cluster are often most useful with ecological data. The usefulness of numerical classification is demonstrated for objective analysis of the data sets resulting from field surveys and monitoring studies conducted for the assessment of effects of pollution.