We present a new visualization of the distance and cluster structure of high dimensional data. It is particularly well suited for analysis tasks of users unfamiliar with complex data analysis techniques as it builds on the well known concept of pie charts. The non-linear projection capabilities of Emergent Self-Organizing Maps (ESOM) are used to generate a topology-preserving ordering of the data points on a circle. The distance structure within the high dimensional space is visualized on the circle analogously to the U-Matrix method for two-dimensional SOM. The resulting display resembles pie charts but has an organic structure that naturally emerges from the data. Pie segments correspond to groups of similar data points. Boundaries between segments represent low density regions with larger distances among neighboring points in the high dimensional space. The representation of distances in the form of a periodic sequence of values makes time series segmentation applicable to automated clustering of the data that is in sync with the visualization. We discuss the usefulness of the method on a variety of data sets to demonstrate the applicability in applications such as document analysis or customer segmentation.