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Geomorphology is the science that studies landscape evolution, thus stands in the centre of the Earth's surface sciences, where, geology, seismology, hydrology, geochemistry, geomorphology, atmospheric dynamics, biology, human dynamics, interact and develop a dynamic system (Murray, 2009). Usually the relationships between the various factors portraying geo-systems are non linear. Neural networks which make use of non–linear transformation functions can be employed to interpret such systems. Applied geomorphology, for example, adaptive environmental management and natural hazard assessment on a changing globe requires, expanding our understanding of earth surface complex system dynamics. The inherent power of self organizing maps to conserve the complexity of the systems they model and self–organize their internal structure was employed, in order to improve knowledge in the field of landscape development, through characterization of drainage basins landforms and classification of recent depositional landforms such as alluvial fans. The quantitative description and analysis of the geometric characteristics of the landscape is defined as geomorphometry. This field deals also, with the recognition and classification of landforms. Landforms, according to Bishop & Shroder, (2004) carry two geomorphic meanings. In relation to the present formative processes, a landform acts as a boundary condition that can be dynamically changed by evolving processes. On the other hand formative events of the past are inferred from the recent appearance of the landform and the material it consists of. Therefore the task of geomorphometry is twofold: (1) Quantification of landforms to derive information about past forming processes, and (2) determination of parameters expressing recent evolutionary processes. Basically, geomorphometry aims at extracting surface parameters, and characteristics (drainage network channels, watersheds, planation surfaces, valleys side slopes e.t.c), using a set of numerical measures derived usually from digital elevation models (DEMs), as global digital elevation data, now permit the analysis of even more extensive areas and regions. These measures include slope steepness, profile and plan curvature, crosssectional curvature as well as minimum and maximum curvature, (Wood, 1996a; Pike, 2000; Fischer et al., 2004). Numerical characterizations are used to quantify