Amyotrophic　lateral　sclerosis,　Parkinson＇s　disease　and　Huntington＇s　disease　are　three　neuro-degenerative　diseases.　In　all　these　diseases,　severe　disturbances　of　gait　and　gait　initiation　are　frequently　reported.　In　this　paper,　we　explore　the　feasibility　of　using　machine　learning　and　statistical　approaches　to　support　the　discrimination　of　these　three　diseases　based　on　gait　analysis.　A　total　of　three　supervised　classification　methods,　namely　support　vector　machine,　KStar　and　Random　Forest,　were　evaluated　on　a　publicly-available　gait　dataset.　The　results　demonstrate　that　it　is　feasible　to　apply　computational　classification　techniques　in　characterise　these　three　diseases　with　the　features　extracted　from　gait　cycles.　Results　obtained　show　that　using　selected　4　features　based　on　maximum　relevance　and　minimum　redundancy　strategy　can　achieve　reasonably　high　classification　accuracy　while　5　features　can　achieve　the　best　performance.　The　continual　increase　of　the　number　of　features　does　not　significantly　improve　classification　performance.　©　2009　Springer-Verlag　Berlin　Heidelberg.