Palynology involves the study of past climatic and environmental changes through changes in the relative frequencies of different pollen types through time. Several multivariate statistical methods are suggested which can help in the description of patterns within pollen data. These techniques are based on comparisons between samples. Samples were compared using the product-moment correlation coefficient computed from data which had been subjected to a centering transformation. The methods are described using a geometric model. If there are m samples and n pollen types, then the data can be regarded as a set of m points in an n-dimensional space. Cluster analysis produces a dendrograph or clustering tree in which samples are grouped with other samples on the basis of their similarity to each other. Principal component analysis produces a set of variates which are linear combinations of the pollen samples, are uncorrelated with each other, and do the best job of describing the data using a minimum number of dimensions. This method is useful in reducing the dimensionality of data sets. Varimax rotation acts on a subset of the principal components to make them easier to interpret. Discriminant analysis is used to find the best way to tell groups of samples apart, where the groups are known a priori. Once a means of discrimination among groups has been established using samples whose groups are known, unknown samples may be classified into the original groups. Canonical analysis produces a way to display the maximum separation between groups in a graphic manner. Examples of applications of these methods in palynology are shown using data from Osgood Swamp, California, and from southern Arizona. These methods offer the advantages of reproducibility of results and speed in pattern description. Once the patterns in the data have been described, however, their interpretation must be done by the palynologist.