Ripple-down rules (RDR) are a way of approaching knowledge acquisition. Knowledge acquisition refers to the transfer of knowledge from human experts to knowledge-based systems. Introductory material Ripple-down rules are an incremental approach to knowledge acquisition and covers a family of techniques. RDR were proposed by Compton and Jansen based on experience maintaining the expert system GARVAN-ES1 (Compton and Jansen 1988). The original GARVAN-ES1 (Horn et al. 1985) employed a knowledge acquisition process, based on Test Driven Development, where new cases that were poorly classified by the system were added to a data base and then used to incrementally refine the knowledge base. The added cases, whose conclusions conflicted with the advice of the system were termed "cornerstone cases". Consequently, the data base grew iteratively with each refinement to the knowledge. The data base could then be used to test changes to the knowledge. Knowledge acquisition tools, similar to those provided by Teiresias were developed to find and help modify the conflicting rules. The tools would display the rules fired by each case and suggestions to "edit" the knowledge to remove the conflicts. In the RDR framework, the human expert's knowledge is acquired based on the current context and is added incrementally. Compton and Jansen argued that the expert's knowledge is to some extent 'made up' to justify why she was right, not to explain how she reached this right interpretation (or conclusion). The justification is based on features that are identified from the current case. The expert creates a rule for classifying cases corresponding to a particular context. This rule is unlikely to classify all cases belonging to the class. Compton and Jansen asserted that it is not possible to create a single elegant context free rule as the knowledge we communicate is a justification in a context. This implies that there is no absolute knowledge that acts as foundation of other knowledge, since knowledge is only true in a context (Compton and Jansen 1990). Methodology Ripple-down rules consist of a data structure and knowledge acquisition scenarios. Human experts' knowledge is stored in the data structure. The knowledge is coded as a set of rules. The process of transferring human experts' knowledge to Knowledge-based systems in RDR is explained in knowledge acquisition scenario. Data structure There are various structures of ripple-down rules, for example single-classification ripple-down rules (SCRDR), multiple-classification ripple-down rules (MCRDR), nested ripple-down rules (NRDR) and repeat-inference multiple-classification ripple-down