The Self-Organising Map is a popular unsupervised neural network model which has successfully been used for clustering various kinds of data. To help in understanding the inﬂuence of single variables or components on clusterings, we introduce a novel method for the visualisation of Component Planes for SOMs. The approach presented is based on the discretisation of the components and makes use of the well-known metro map metaphor. It depicts consistent values and their ordering across the map for discretisations of various components and their correlations in terms of directions on the map. In our approach Component Lines are drawn for each component of the data, allowing the combination of numerous Component Planes into one plot. We also propose a method to further aggregate these Component Lines, by grouping highly correlated variables, i.e. similar lines on the map. To show the applicability of our approach we provide experimental results for two popular machine learning data sets.