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In practical data mining problems high-dimensional data has to be analyzed. In most of these cases it is very informative to map and visualize the hidden structure of complex data set in a low-dimensional space. The aim of this paper is to propose a new mapping algorithm based both on the topology and the metric of the data. The utilized Topology Representing Network (TRN) combines neural gas vector quantization and competitive Hebbian learning rule in such a way that the hidden data structure is approximated by a compact graph representation. TRN is able to define a low-dimensional manifold in the high-dimensional feature space. In case the existence of a manifold, multidimensional scaling and/or Sammon mapping of the graph distances can be used to form the map of the TRN (TRNMap). The systematic analysis of the algorithms that can be used for data visualization and the numerical examples presented in this paper demonstrate that the resulting map gives a good representation of the topology and the metric of complex data sets, and the component plane representation of TRNMap is useful to explore the hidden relations among the features.