The properties of additively manufactured parts significantly depend on the instantaneous temperature distribution and cooling rates in the melt pool and surrounding regions, respectively. The open-loop control of additive manufacturing (AM) cannot compensate for the stochastic variations in the melt pool temperature and size during the process. Closed-loop control can play a significant role in maintaining a desired melt pool temperature by controlling the laser power in situ. However, in order to effectively implement closed-loop control, two critical parameters need to be calculated such as the melt pool temperature and melt pool size. To calculate the melt pool temperature, in this work, a thermal imaging camera calibrated via the emissivity value of the material and validated through a Pyrometer was used. To calculate the melt pool size, two machine vision algorithms are implemented. It was found that the connected component labeling algorithm performed better than the Canny edge detection algorithm.