The research reported here deals with the early characterization of Parkinson’s disease (PD), the second most common degenerative disease of the human motor system after Alzheimer’s. PD results from the death of dopaminergic neurons in the substantia nigra region of the brain. Its occurrence is highly correlated with the aging population whose numbers increase with the healthcare benefits of a longer life. Observation of motor control symptoms associated with PD, such as gait and speech analysis, is most often used to evaluate, detect, and diagnose PD. Since speech and some delicate motor functions have provided early detection signs of PD, reliable analysis of these features is a promising objective diagnostic technique for early intervention with any remedial measures. We implement and study here three PD diagnostic methods and their correlation between each other’s results and with the motor functions in subjects diagnosed with and without PD. One initial test documented well in the literature deals with feature analysis of voice during phonation to determine dysphonia measures. Features of the motor function of two fingers were extracted in tests titled “Motor function of alternating finger tapping on a computer keyboard” and “Motor function of the index and thumb finger tapping with an accelerometer”, that we objectively scripted. The voice dysphonia measures were extracted using various software packages like PRAAT, Wavesurfer, and Matlab. In the initial test, several robust feature selection algorithms were used to obtain an optimally selected subset of features. We were able to program distance classifiers, support vector machine (SVM), and hierarchical clustering discrimination approaches for the dichotomous identification of non-PD control subjects and people with Parkinson’s (PWP). Validation tests were implemented to verify the accuracy of the classification processes. We determined the extent of functional agreement between voice and motor functions by correlating test results.