In this chapter, we will review basic techniques from knowledge discovery in databases (KDD), data mining (DM), and machine learning (ML) that are suited for applications in predictive toxicology. We will discuss primarily methods which are capable of providing new insights and theories. Methods, which work well for predictive purposes but do not return models that are easily interpretable in terms of toxicological knowledge (e.g., many connectionist and multivariate approaches), will not be discussed here, but are discussed elsewhere in this book.