Decision trees are generally accepted as a well-known data classification technique for their efficiency, ease of use and relatively low complexity. A decision tree can be conveniently converted into a set of classification rules. This article introduces data mining emphasizing the use of decision trees, including well-known algorithms such as 1D3, CART, C4.5 and C5.0, and then examines their advantages and disadvantages. Finally, applications of decision trees are discussed in various domains such as financial analysis, Web information extraction, remote sensing image mining, Thai word extraction and Website failure diagnosis.