The kNN algorithm remains a popular choice for pattern classification till date due to its non-parametric nature, easy implementation and the fact that its classification error is bounded by twice the Bayes error. In this paper, we show that the performance of the kNN classifier improves significantly from the use of (training) class-wise group-statistics based two criteria during pairwise comparison of features in a given dataset. Granger causality is employed to assign preferences to each criteria. Analytic Hierarchy Process (AHP) is applied to obtain weights for different features from the two criteria and their preferences. Finally, these weights are used to build a weighted distance function for the kNN classification. Comprehensive experimentation on fifteen benchmark datasets of the UCI Machine Learning Repository clearly reveals the supremacy of the proposed Granger causality driven AHP induced kNN algorithm over the IcNN method with many different distance metrics, and, with various feature selection strategies. In addition, the proposed method is also shown to perform well on high-dimensional face and hand-writing recognition datasets.