In this work, we propose a method that detects and tracks the tip of tools used in microsurgical training. This method can be used to provide valuable metrics regarding the surgeon's hand movement. It can benefit the training of surgeons, given the steep learning curve in microsurgery. Unlike past research, our tool tracking algorithm does not rely on color based measurements. Thus, it can be used in a broader domain. Also, our approach is robust to surrounding environments with non-static background, where background subtraction techniques are not suitable. Experimental results show that the proposed tool localization method has high accuracy and is statistically reliable.