This feasibility and validation study addresses the potential use of magnetic resonance imaging (MRI) texture analysis of the pancreas in patients with chronic pancreatitis (CP). Extraction of 851 MRI texture features from diffusion weighted imaging (DWI) of the pancreas was performed in 77 CP patients and 22 healthy controls. Features were reduced to classify patients into subgroups, and a Bayes classifier was trained using a tenfold cross-validation forward selection procedure. The classifier was optimized to obtain the best average m-fold accuracy, sensitivity, specificity, and positive predictive value. Classifiers were: presence of disease (CP vs. healthy controls), etiological risk factors (alcoholic vs. nonalcoholic etiology of CP and tobacco use vs. no tobacco use), and complications to CP (presumed pancreatogenic diabetes vs. no diabetes and pancreatic exocrine insufficiency vs. normal pancreatic function). The best classification performance was obtained for the disease classifier selecting only five of the original features with 98% accuracy, 97% sensitivity, 100% specificity, and 100% positive predictive value. The risk factor classifiers obtained good performance using 9 (alcohol: 88% accuracy) and 10 features (tobacco: 86% accuracy). The two complication classifiers obtained similar accuracies with only 4 (diabetes: 83% accuracy) and 3 features (exocrine pancreatic function: 82% accuracy). Pancreatic texture analysis demonstrated to be feasible in patients with CP and discriminate clinically relevant subgroups based on etiological risk factors and complications. In future studies, the method may provide useful information on disease progression (monitoring) and detection of biomarkers characterizing early-stage CP.