This thesis investigates clinical phase recognition for cardiac catheterization purposes, focusing on coronary angiography (CAG) procedures, in the context of an increasing annual prevalence of coronary artery disease. It applies machine learning to analyze C-arm logs and video recordings, aiming to improve procedural efficiency by recognizing procedural phases. Key findings include a baseline model with 45% accuracy; an 80.73% accurate C-arm model, including predictions only for operative phases; a 63.8% accurate object detection model, effective in initial and final phases but less so in operational stages; a combined data model with 79.46% accuracy, showing enhanced performance; and a reduced granularity model achieving 88.23% accuracy, highlighting a balance between granularity and performance. The combined model promises effective CAG phase recognition, especially with reduced granularity, offering comprehensive coverage and improved accuracy. However, it faces challenges in predicting specific phases accurately. The study recognizes limitations like the random forest model's non-temporal nature and potential information loss from object detection. Future research should expand datasets, explore temporal data models, and optimize the balance between granularity and performance for clinical applicability. In conclusion, the thesis contributes to SPR in cardiac catheterization labs, particularly in CAG, demonstrating the potential of machine learning models using C-arm and video data for phase recognition, which could enhance operational efficiency and patient care quality in cardiovascular interventions. Further research is needed to refine these models for clinical use.