During the recent years, landform detection and mapping has been one of the most active fields of geomorphometry. However, there is still a need for quantitative work addressing the issue of classifying repeating landform types (MacMillan et al., 2004). The main issue in developing an accurate automatic classification algorithm of repetitive landform types is given by the difficulty to integrate contextual information within the analysis (Evans, 2012). Therefore, the motivation of the current approach is strongly related to the importance of context analysis in the field of specific geomorphometry. Introduced in landscape ecology to evaluate the spatial structure of a landscape, the concept of landscape metrics embraces a series of specific indicators for quantifying topological and contextual information. Thus, considering the fact that the assessment of topological and contextual attributes is not possible based on local, statistical and regional land-surface variables, the main objective of this study is to assess the applicability of landscape metrics for the delineation of landform patterns. The quantification of landscape metrics involved the segmentation and classification of the following morphometric variables: elevation, profile curvature and local relief. Using an unsupervised method, the Iso Cluster Unsupervised Classification tool from ArcGIS10® software, a total of 24 classes have been used in order to fulfill the minimum requirement imposed by the concept of landscape metrics and further statistical analysis. In order to test the transferability degree of landscape metrics among different dune fields, a set of statistical analyses was carried out. The proposed methodology has been applied on freely available ASTER GDEM’s. This paper provides new prospects regarding the applicability of landscape metrics for the delineation of landform patterns.