Primate visual object recognition is supported by critical neural circuits housed in the inferior temporal (IT) cortex. How similar is one macaque IT to another? This study investigates inter-individual variation in IT cortical representations by analyzing neural responses to 640 visual stimuli (100 ms presentation at 8 degrees) across eight macaque monkeys. We employed two complementary analytical approaches: a direct one-to-one neuron matching method to assess exact neural correspondence and a many-to-one linear regression framework to evaluate potential linear transformations between individual IT representations. To establish a theoretical framework for interpreting inter-individual variation, we leveraged ResNet-50 activations, an artificial neural network (ANN) that aligns well with ventral stream processing. We generated synthetic datasets representing different hypothetical scenarios: identical copies, linear transformations, nonlinear transformations, and a mix of linear and nonlinear transforms of neural representations. This framework allowed us to systematically evaluate the nature and extent of idiosyncratic neural responses across individuals. Our findings reveal three key insights: First, the one-to-one neuronal matching analysis demonstrated a partial but incomplete correspondence between individuals, ruling out the hypothesis of purely identical IT representations across monkeys. Second, a significant non-zero one-to-one match suggests the existence of some nearly identical neural responses across individuals. Third, the many-to-one analysis yielded approximately 60\% explained variance (between individual monkey ITs), even after extrapolating for sampling limitations, indicating that inter-individual differences cannot be fully explained by linear transformations alone. These results suggest that IT representations across individuals comprise conserved elements and linearly and nonlinearly transformed components, with important implications for understanding individual differences in visual processing. Our findings establish critical ceiling estimates for computational model alignment with primate visual systems and emphasize the importance of robust methodology and adequate sample sizes in comparative neuroscience, particularly when evaluating ANNs as models of primate visual processing.