Amyotrophic Lateral Sclerosis (ALS), is a fatal, progressive, neurodegenerative disease that causes selective loss of motor neurons. Approximately 5-10% of ALS cases have a known family history of ALS (fALS). Sporadic ALS (sALS) represents 90-95% of cases of ALS and the related studies are limited in the literature due to difficulties in modelling sALS with mouse models. Human induced pluripotent stem cell (iPSC) technology is used to conduct studies on pathology and disease mechanisms in neuronal cells. In this study, we investigate how well we can distinguish between healthy and ALS motor neurons using neuronal images. These images provide snapshots of the neuronal cells of multiple ALS patients together with multiple healthy control samples. Our preliminary approach involves decomposing an image into a grid of patches and feeding them through a transformer-based feature extractor to obtain a feature set. These features are then clustered to identify separable clusters/regions corresponding to ALS and control features. The results indicate a promising separation in some modalities which may be further emphasized in subsequent research to obtain a clearer separation. Further analysis will be conducted to identify distinct features that correspond to ALS and control images and a classification model will be trained to differentiate between ALS and control neuronal images. In conclusion, neuronal images extracted from iPSC provide a comprehensive experimental foundation to explore the underlying factors of ALS.