This paper explores the importance of using optimisation techniques when tuning a machine learning model. The hyperparameters that need to be determined for the Artificial Neural Network (ANN) to work most efficiently are supposed to find a value that achieves the highest recognition accuracy in a face recognition application. First, the model was trained with manual optimisation of the parameters. The highest recognition accuracy that could be achieved was 96.6% with a specific set of parameters used in the ANN. However, the error rate was at 30%, which was not optimal. After utilising Grid Search as the first automated tuning method for hyperparameters, the recognition accuracy rose to 96.9% and the error rate could be minimised to be less than 1%. Applying Random Search, a recognition accuracy of 98.1% could be achieved with the same error rate. Adding further optimisation to the results from Random Search resulted in receiving an accuracy of 98.2%. Hence, the accuracy of the facial recognition application could be increased by 1.6% by applying automated optimisation methods. Furthermore, this paper will also deal with common issues in face recognition and focus on potential solutions.