Drug manufacturing processes must consistently deliver safe and effective product. A key part of achieving this is process validation utilizing Quality by Design (QbD) principles. To meet process validation requirements, process characterization (PC) studies are often performed to expand process understanding and establish an appropriate control strategy that enables the manufacturing process to consistently deliver a target product profile. Two key elements of the control strategy resulting from PC work are a list of critical process parameters (CPPs) and defined operating ranges (ORs). These are frequently derived based on mathematical models describing the relationship between process parameters and critical quality attributes (CQAs). Risk assessment and design of experiments (DOE) techniques are effectively deployed in the industry to identify parameters to study and build process understanding. However, traditional data analysis techniques do not fully utilize the data produced by these studies. In particular, stepwise regression algorithms based on p-values are prone to generate false positives and overfit data, potentially leading to unnecessarily complex control strategies. Many of the deficiencies of traditional stepwise regression can be alleviated by applying cross validation to stepwise regression algorithms, as well as Monte Carlo simulations to estimate model accuracy and predict CQA distributions. These methods can greatly enhance process understanding and assist in the selection of CPPs. A series of PC studies were performed in bioreactors to evaluate a process to produce a recombinant monoclonal antibody. The studies examined process parameters such as dissolved oxygen, pH, temperature, inoculation density, as well as cell density at two key process steps. The resulting data were analyzed using several Monte Carlo based methods. First, cross validation was used to determine model size and select parameters to be included in the model. Next, Monte Carlo cross validation was used to compare the accuracy of different models. Finally, simulated CQA profiles were generated to validate proposed ORs. This workflow provides greater process understanding based on a given PC data set and provides higher statistical confidence in both CPP selection and establishment of a control strategy.