Both scientists and normal users face enormous amounts of data, which might be useless if no insight is gained from it. To achieve this, visualization techniques can be used. Many datasets have a dimensionality higher than three. Such data is called “hypervariate” and cannot be visualized directly in the three-dimensional space that we inhabit. Therefore, a wide variety of specialized techniques have been created for rendering hypervariate data. These techniques are based on very different principles and are designed for very different areas of application. This paper gives an overview of six representative techniques. For most techniques a rendering of a common dataset is provided to allow an easier comparison. Furthermore, an evaluation of the strengths and weaknesses of each technique is given. As an outlook, two papers dealing with quantitative analysis of visualization methods are presented. Hypervariate Data Visualization