Computer vision systems designed for flotation froth image analysis are well established in industry, where their ability to measure froth flow velocities and stability are used to control recovery. However, the use of froth image analysis to estimate the concentrations of mineral species in the froth phase is less well established and the reliability of these algorithms depends on the quality of the features that can be extracted from the froth images. Over less than a decade, convolutional neural networks have significantly pushed the boundaries with regard to image recognition in range of technical applications, notably cancer diagnosis, face recognition, remote sensing, as well as applications in the food industry. With the exception of the exploration geosciences, they are yet to make meaningful inroads in the mineral process industries. In this study, the use of three pretrained neural networks architectures to estimate froth grades from industrial image data, namely AlexNet, VGG16 and ResNet is considered. In its pretrained format, AlexNet outperformed previously proposed methods by a significant margin. This margin could be increased markedly via partial retraining of the VGG16 and ResNet34 networks.