To assess the gait variability in patients with Parkinson's disease (PD), we first used the nonparametric Parzen-window method to estimate the probability density functions (PDFs) of stride interval and its two subphases (i.e., swing interval and stance interval). The gait rhythm standard deviation (sigma) parameters computed with the PDFs indicated that the gait variability is significantly increased in PD. Signal turns count (STC) was also derived from each outlier-processed gait rhythm time series to serve as a dominant feature, which could be used to characterize the gait variability in PD. Since it was observed that the statistical parameters of swing interval or stance interval were highly correlated with those of stride interval, this article only used the stride interval parameters, i.e., sigma(r) and STC(r) , to form the feature vector in the pattern classification experiments. The results evaluated with the leave-one-out cross-validation method demonstrated that the least squares support vector machine with polynomial kernels was able to provide a classification accurate rate of 90.32% and an area (Az) of 0.952 under the receiver operating characteristic curve, both of which were better than the results obtained with the linear discriminant analysis (accuracy: 67.74%, Az: 0.917). The features and the classifiers used in the present study could be useful for monitoring of the gait in PD.