The analysis of gait rhythm by pattern recognition can support the state-of-the-art clinical methods for the identification of neurodegenerative diseases (NDD). In this study, we investigated the use of time domain (TD) and time-dependent spectral features (PSDTD) for detecting NDD sub-types. Also, we proposed two classification pathways for supporting NDD diagnosis, the first one made by a two-step learning phase, whereas the second one encompasses a single learning model. We considered stride-to-stride fluctuation data of healthy controls (CN), patients affected by Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (AS). TD feature set provided good results to distinguish between CN and NDDs, while performances lowered for specific NDD identification. PSDTD features boosted the accuracy of each binary identification task. With k-nearest neighbor classifier, the first diagnosis pathway reached 98.76% accuracy to distinguish between CN and NDD and 94.56% accuracy for NDDs sub-types, whereas the second pathway offered an overall accuracy of 94.84% for a 4-class classification task. Outcomes of this study indicate that the use of TD and PSDTD features, simple to extract and with a low computational load, provides reliable results in terms of NDD identification, being also useful for the development of gait rhythm computer-aided NDD detection systems.