Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease that affects the nervous system causing muscle weakness, paralysis, leading to death. Given that abnormalities in spinal motoneuron (MN) excitability begin long before symptoms manifest, developing an approach that could recognize fluctuations in MN firing could help in early diagnosis of ALS. This paper introduces a machine learning approach to discriminate between ALS and normal MN firing. The approach is based on two electrophysiological markers; namely, spiking latency and the spike-triggered average signal. The method is examined using data generated from a computational model under systematic variation of MN properties. Such variations mimic the differential dynamic changes in cellular properties that different MN types experience during ALS progression. Our results demonstrate the ability of the approach to accurately recognize ALS firing patterns across the spectrum of examined variations in MN properties.Clinical Relevance- These results represent a proof of concept for using the proposed machine-learning approach in early diagnosis of ALS.