This article concerns itself with exploratory data analysis in hyperspace, and discusses a method of data reduction intended to allow insight into the geometry of ap-dimensional data set wherep> 2. The essential idea is to examine various “views” through the data set using a suitably chosen line, the baseline, for defining a viewing direction. For a given data point, its distance from a suitable point on the baseline, called the vertex, and the angular separation between the baseline and the line connecting the vertex to the data point are taken as coordinates of an ordinary polar coordinate plot in two dimensions. The bulk of the article discusses empirical evidence for the utility of such a plot, which can be referred to as a coneplot, because points are essentially identified with the intersection inpdimensions of a hypersphere and a ray of a hypercone.