In-situ measurements provide vast information for additive manufacturing process understanding and real-time control. Data from various monitoring techniques observe different characteristics of a build process. Fusing multi-modal in-situ monitoring data can significantly enhance process anomaly detection, part defect prediction, and build failure diagnosis, thus improving AM part quality control. This paper compares the powder bed fusion in-process observations from two types of AM in-situ monitoring, coaxial melt pool imaging, and layerwise imaging, and investigates the correlation between the two observations for a build of parts with multiple geometric features and scan patterns. All data were collected from an open architecture powder bed fusion AM testbed. Data analysis shows that both datasets exhibit significant statistical changes when new features are introduced during the build. However, further machine learning-based modeling indicates that statistical features extracted from the two data sets do not correlate very well. Discussions are provided on how the statistical analysis of the observations from the two modality monitoring system can be utilized for data fusion strategy development, especially toward improving process anomaly detection.