Decision-tree induction algorithms are highly used in a variety of domains for knowledge discovery and pattern recognition. They have the advantage of producing a comprehensible classification/regression model and satisfactory accuracy levels in several application domains, such as medical diagnosis and credit risk assessment. In this chapter, we present in detail the most common approach for decision-tree induction: top-down induction (Sect. 2.3). Furthermore, we briefly comment on some alternative strategies for induction of decision trees (Sect. 2.4). Our goal is to summarize the main design options one has to face when building decision-tree induction algorithms. These design choices will be specially interesting when designing an evolutionary algorithm for evolving decision-tree induction algorithms.