In Market Research, data which is multivariate in nature is often generated. For example, in a Brand Association study the associations of brands with a variety of attributes are measured and in a Usage and Attitudes survey information regarding the products used in different market segments is gained. In order to see the messages in the data clearly and to communicate them to others with maximum impact, it is desirable to display them in a visual form, e.g. by using graphs. However, the commonly-used graphical displays are univariate in nature, i.e. they deal with the dimensions one at a time. This means that some of the information is lost, and also many different graphs may be needed where there are a large number of dimensions. Multivariate mapping techniques to overcome this problem have been available for some years. However, they do not appear to be widely utilised in Market Research, partly because of non-accessibility (suitable computer software may not be immediately available and easy to use) and partly because of lack of confidence on the part of potential users. People may not be confident in using the techniques, and may also fear that the output will not be understood by their target ("non-specialist") audience. This paper shows the authors' experience with two techniques : Correspondence Analysis and Covariance Biplot (Principal Components Plot). The exposition is completely non-mathematical and shows our use of the techniques in some practical examples. In particular we describe some of the enhancements which we have found useful in communication to non- specialist audiences. The aim of the paper is to stimulate discussion with other market researchers, and hopefully to increase the appropriate use of these methods in the industry as a whole. It would also be useful if more user- friendly software might become available as a result of increased interest and use.