This paper explores various approaches to determine the time of an analogue watch by developing two systems using the design and creation method. The aim is to see to what extent a computer can determine the time, by comparing the two systems, as well as how it can deal with contextual set-up variations such as design, orientation and lighting conditions. The first system uses OpenCV to find the watch hands and geometry to calculate the time. The second system uses Machine Learning by building a Neural Network to classify images in Tensorflow using a multi–labelling approach. The results show that in a set environment the geometric system performs better than the Machine Learning model. The geometric system predicted correctly with an accuracy of 80% whereas the best Machine Learning model got 74%. The accuracy of the model did increase when adding data augmentation, however there was no significant difference when further adding synthetic data. When using contextual set-up variations, the model performed poorly with 21%.