The concept of mental workload has received much attention when it comes to multidimensional evaluation of human performance in complex environments. This is especially important in military settings as modern technology and increasing battlefield demands increasingly impact operators' mental workload. Military personnel training must reflect cognitive demands of these real-world environments and trainee experiences of workload should match on-the-job experiences. Most current training systems, including virtual simulations, default to subjective workload measures as the singular data source assessing training effectiveness. Conversely, other emerging research focuses on objective workload measures (i.e., physiological) as indicators of performance and training effectiveness. Our research examined both approaches to measuring trainee workload while operating Predator Research Integrated Networked Combat Environment (PRINCE), a remotely piloted aircraft training simulator. Twelve student pilots from the School of Aviation at the University of North Dakota participated in novel training scenarios within PRINCE as pilot and sensor operators. Brain wave activity was recorded using Advance Brain Monitoring's head-mounted B-Alert X-24 and X-10 systems, capturing electroencephalogram, electrocardiogram, and electrooculography activity throughout training missions. Cardiac rhythms and eye movements were captured with B-Alert as measures of engagement, distraction and fatigue. Intific's Neurobridge software captured and attempted integration of objective workload data with objective simulation performance data collected using the Performance Evaluation and Tracking System. Self-report measures of workload were also gathered immediately following each training scenario. Subjective ratings of workload were significantly different between pilot and sensor operator roles and mission difficulty. Engagement, distraction, and fatigue were significantly different for scenarios, with the most difficult scenario showing the lowest distraction rate, highest workload, and lowest performance success. Results shed light on the congruency between subjective and objective measures of workload. These measures offer insight into capturing, synthesizing, and characterizing multifaceted workload data to better understand how workload relates to performance during training.