We describe our modifications of the technique of parallel coordinate plot for supporting visual exploration of object classes, in particular, resulting from cluster analysis. We strived at creating a tool that would be suitable for analysis of large datasets. The basic parallel coordinate plot technique with the traditional method for representing classes, multi-coloured brushing, fails to properly convey class-relevant information due to tremendous overlapping of lines. We have applied two general approaches to handling large amounts of data: aggregation and filtering. Thus, information concerning the distribution of characteristics in classes and the entire dataset is shown on parallel coordinates in an aggregated form. This is combined with displaying individual characteristics only for user-selected object subsets.