Introduction: Preeclampsia (PE) and fetal growth restriction (FGR) are common obstetrical complications, often with pathological features of maternal vascular malperfusion (MVM) in the placenta. Current placental clinical pathology methods involve a manual visual examination of histology sections, a practice that can be resource-intensive and demonstrate moderate-to-poor inter-pathologist agreement on diagnostic outcomes, dependant on the degree of pathologist sub-specialty training. Methods: This thesis aims to apply different machine learning (ML) feature extraction methods to classify digital images of placental histopathology specimens, collected from PE, FGR, PE + FGR, and healthy pregnancies, according to the presence or absence of MVM lesions. 166 digital images were captured from histological placental specimens, manually scored for MVM lesions (MVM- or MVM+) and used to develop various support vector machine (SVM) classifier models, differing in feature extraction methods. Classification performance of each model was assessed through accuracy, precision, and recall using confusion matrices. Results: SVM models demonstrated accuracies between 47-73% in MVM classification, with poorest performance observed on images with borderline MVM presence, as determined through manual observation. Data augmentation provided little to no improvement to the accuracies. Conclusion: The results are promising for the integration of ML methods into the placental histopathological examination process. Using this study as a proof-of-concept foundation will lead our group and others to carry ML models further in placental histopathology.