Today's state-of-the-art expert systems are plagued by four major problems: brittleness, lack of metaknowledge, knowledge acquisition, and validation. Knowledge acquisition by itself is a very time consuming and tedious process. The uncertainty of information and erroneous data have also caused knowledge engineers anxious moments. In order to address these problems, several machine learning techniques supported by well-formulated theories and algorithms, have been developed. In this article some of these techniques are reviewed along with examples of their application to civil engineering problems. The techniques presented either fall under the category “learning from examples” (commonly referred to as inductive learning) including the ID3 algorithm, the rough sets theory, and the PROTOS algorithm, or “learning from observations” (also known as conceptual clustering) including the COBWEB algorithm, or a combination of both.