A map of multi-dimensional data is a graphical representation - defined in 2D or 3D space - of a set of data points that reflects similarity relationships amongst them. Triangulations of those points can be produced to generate surface meshes on which additional information can be mapped to visual attributes such as color or height. Such surfaces may then be used to explore the data set and the similarities between the different data points. In this paper we introduce Voromap, an exploration tool that is based on Voronoi diagrams of the projected data, and compare that representation with a standard representation that uses triangulations. Voronoi diagrams represent each element in the data set by the area of a polygon in which every point within the polygon is closer to its generating point than to any other point from the data set. A user experiment was conducted to compare Voronoi maps with equivalent maps represented by surface triangulations explored with a similar triangulation-based tool.