This paper adopts dimensionality reduction, clustering and information visualization techniques to organize and map a set of 86 chemical elements (CE). Each CE is characterized through 17 properties and the dissimilarities between the CE are measured by different metrics, namely the arccosine, Canberra and Lorentzian distances. The dissimilarity information is then used as input to hierarchical clustering and multidimensional scaling algorithms. The two computational techniques yield 2- and 3-dim maps of the CE organized accordingly to their characteristics. The results can be visualized and interpreted not only under the light of the CE location, but also of the emerging clusters. The new representations constitute an alternative to the classic Cartesian-like 2-dimensional table and point towards advanced scientific visualization using present day computational resources.