The nearest neighbor technique is a simple and appealing approach to pattern classification. It relies on the assumption of locally constant class conditional probabilities. This assumption however becomes invalid in high dimensions with finite samples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose a technique that computes a locally flexible metric based on Chi-squared distance analysis to try to minimize bias. Our method produces neighborhoods that are highly adaptive to query locations: neighborhoods are elongated along less relevant feature dimensions, and constricted along most influential ones. As a result, the class conditional probabilities tend to be smoother in the modified neighborhoods, whereby better classification performance can be achieved. The efficacy of our method is validated and compared against other techniques using both simulated and real world data.