  Multivariate statistical methods are widely used throughout the sciences,
including microscopy, however, their utilisation for analysis of electron
backscatter diffraction (EBSD) data has not been adequately explored. The basic
aim of most EBSD analysis is to segment the spatial domain to reveal and
quantify the microstructure, and links this to knowledge of the crystallography
(eg crystal phase, orientation) within each segmented region. Two analysis
strategies have been explored; principal component analysis (PCA) and k-means
clustering. The intensity at individual (binned) pixels on the detector were
used as the variables defining the multidimensional space in which each pattern
in the map generates a single discrete point. PCA analysis alone did not work
well but rotating factors to the VARIMAX solution did. K-means clustering also
successfully segmented the data but was computational more expensive. The
characteristic patterns produced by either VARIMAX or k-means clustering
enhance weak patterns, remove pattern overlap, and allow subtle effects from
polarity to be distinguished. Combining multivariate statistical analysis (MSA)
approaches with template matching to simulation libraries can significantly
reduce computational demand as the number of patterns to be matched is
drastically reduced. Both template matching and MSA approaches may augment
existing analysis methods but will not replace them in the majority of
applications.
