Characterizing graphite shape in cast irons is of importance for control of cast components. This is still done based on standard charts and has been more and more supported by quantitative image analysis during the last decades. Despite the significant progresses made by image analysis techniques and software packages, the results of quantitative image analysis are still moderately satisfactory, and the question raised of the capabilities of machine learning to supplement this approach. This work first presents a review on graphite shape analysis by standard image analysis and also lists the very few works that have used machine learning approach. Then a series of challenging images in which both lamellar, compacted and spheroidal graphite coexist was submitted to standard image analysis and to a convolutional neural network, a deep learning model. The comparison of the two methods shows that machine learning produced very encouraging results when compared to standard image analysis, in particular for what concerns the presence of lamellar graphite