While the persistent data of many advanced database applications, such as OLAP and scientific studies, are characterized by very high dimensionality, typical queries posed on these data appeal to a small number of relevant dimensions. Unfortunately, the multi-dimensional access methods designed for high-dimensional data perform rather poorly for these partially specified queries. A potentially very appealing idea, frequently suggested in the literature, is to adopt a node-splitting policy that takes into account the "importance" of individual dimensions, which could be determined either a priori or through a statistical sampling of actual queries. This paper presents the results of some carefully controlled experiments conducted to observe the effects of query-based splitting on the performance of KDB-trees. The strategy is compared to a splitting policy that selects the split dimensions in a "cyclic" fashion, which has been shown to be very effective, especially in high-dimensional situations. Based on the results, the query-based splitting does not appear to be a very appealing splitting strategy for KDB-trees.