In this article, we investigate four variations ~D-HS M , D-HS W , D-HS E , and D-HS EW ! of a novel indexing technique called D-HS designed for use in case-based reasoning ~CBR! systems. All D-HS modifications are based on a matrix of cases indexed by their discretized attribute values. The main differences between them are in their attribute discretization stratagem and similarity determination metric. D-HS M uses a fixed number of intervals and simple intersection as a similarity metric; D-HS W uses the same discretization approach and a weighted intersection; D-HS E uses information gain to define the intervals and simple intersection as similarity metric; D-HS EW is a combination of D-HS E and D-HS W . Benefits of using D-HS include ease of case and similarity knowledge maintenance, simplicity, accuracy, and speed in comparison to conventional approaches widely used in CBR. We present results from the analysis of 20 case bases for classification problems and 15 case bases for regression problems. We demonstrate the improvements in accuracy and/or efficiency of each D-HS modification in comparison to traditional k-NN, R-tree, C4,5, and M5 techniques and show it to be a very attractive approach for indexing case bases. We also illuminate potential areas for further improvement of the D-HS approach.