We examine the relation between the sounds emitted by the knee joint during walking and its condition, with particular focus on osteoarthritis, and investigate their potential for noninvasive detection of knee pathology. We present a comparative analysis of several features and evaluate their discriminant power for the task of normal-abnormal signal classification. We statistically evaluate the feature distributions using the two-sample Kolmogorov-Smirnov test and the Bhattacharyya distance. We propose the use of 11 statistics to describe the distributions and test with several classifiers. In our experiments with 249 normal and 297 abnormal acoustic signals from 40 knees, a Support Vector Machine with linear kernel gave the best results with an error rate of 13.9%.