Non-parametric approaches are being used in various fields to address classification type problems, as well as to estimate continuous variables. One type of the non-parametric lazy learning algorithms, a k-Nearest Neighbor (k-NN) algorithm has been applied to estimate water retention at –33 and –1500 kPa matric potentials from soil texture, and optionally bulk density and/or organic matter content data. Different ‘design-parameter’ settings, analogous to model parameters have been optimized and the response of the algorithm has been tested on alternative data sets. The k-NN technique showed little sensitivity to potential sub-optimal settings, as long as extremes were avoided. It also proved to be a robust technique that produces reliable estimation results when used on different data sets. Performance of the algorithm has compared well to the performance of a neural network model, developed using the same data and input soil attributes. The k-NN technique is a competitive alternative to other techniques to develop pedotransfer functions (PTFs), especially since re-development of PTFs is not necessarily needed as new data become available. We developed a computer tool – that is publicly available - that uses the above algorithm to estimate soil water retention at –33 and –1500 kPa matric potentials. We describe the rationale behind the k-NN estimation of soil water retention as well as give account of the most important features of this tool.