Schizophrenia and other neurodevelopmental disorders often have very heterogeneous symptoms, especially regarding cognition: while some individuals may exhibit deficient cognition, others are relatively unaffected. Studies using developmental animal models often ignore phenotypic heterogeneity in favour of traditional treatment/control comparisons. This may result in resilient or unaffected individuals masking the effects of susceptible individuals if grouped together. Here, we used maternal immune activation and limited bedding and nesting, respectively, as a two-hit neurodevelopmental model for schizophrenia. Both factors reduced cognitive function in a novel object recognition (NOR) task. While we found treatment group effects on cognitive phenotypes, behavioural clustering identified three subpopulations exposed to either insult: those exhibiting ‘typical’ cognitive performance on the NOR, an intermediate phenotype, or a marked deficit. These clusters included offspring from each treatment group, although both intermediate and marked deficit clusters were composed primarily of offspring from treated groups. Clustering allowed stratification within treatment groups into ‘susceptible’ and ‘resilient’ individuals, while also identifying conserved phenotypes across treatment groups. Using unbiased cluster analyses in preclinical models can better characterize phenotypes and enables a better understanding of both face and construct validity of phenotypic heterogeneity. The use of unbiased clustering techniques may help identify potential markers associated with individual susceptibility and resilience in neurodevelopmental disorder models.