Objective biomarkers for Parkinson’s disease (PD) are needed, and a PD MRI diagnostic could have high impact in clinical and research applications. 3T MRI sequences sensitive to neuromelanin loss and iron accumulation in substantia nigra pars compacta and locus coeruleus robustly detect PD effects. We hypothesized that a multivariate MRI classifier can differentiate PD from controls with high accuracy. A machine learning classifier was developed using data from PD and controls (n=67) with brainstem MRI and demographic features as model inputs. Using 5-fold cross-validation the model demonstrated 86% accuracy, which is in a clinically useful range and warrants further development.