Decision Tree Classifiers (DTC's) are used successfully in many diverse areas such
as radar signal classification, character recognition, remote sensing, medical diagnosis, expert
systems, and speech recognition, to name only a few. Perhaps, the most important feature of
DTC's is their capability to break down a complex decision-making process into a collection of
simpler decisions, thus providing a solution which is often easier to interpret. This paper presents a
survey of current methods for DTC designs and the various existing issues. After considering
potential advantages of DTC's over single stage classifiers, the subjects of tree structure design,
feature selection at each internal node, and decision and search strategies are discussed. Some
remarks concerning the relation between decision trees and Neural Networks (NN) are also made