Self-Organising Maps have been used for a wide range of clustering applications. They are well-suited for various visualisation techniques to offer better insight into the clusterings. A particularly feasible visualisation is the plotting of single components of a data set and their distribution across the SOM. One central problem of the visualisation of Component Planes is that a single plot is needed for each component, which leads to problems with higher-dimensional data. We therefore build on the Metro Visualisation for Self-Organising Maps which integrates Component Planes into one illustration. Higher-dimensional data sets still pose problems in terms of over-loaded visualisations - component selection and aggregation techniques are highly desirable. Hence, we propose and compare two methods, one for the aggregation of correlated components, one for the selection of the components that are most feasible for visualisation with respect to a certain SOM clustering.