A computer-implemented method of forming a neural network for detecting surface defects in aircraft engine components, the method including: providing (i) a pre-trained deep learning network having, in sequence, an input layer for receiving pixel values of pixelated images of objects, a set of convolutional layers, a set of fully-connected layers, and an output layer, wherein the pre-trained deep learning network has fixed weights and is pre-trained, and (ii) an extreme learning machine network having, in sequence, an input layer, at least one hidden layer, and an output layer, wherein the values of the nodes of the input layer of the extreme learning machine network derive from the values of the nodes of one of the convolutional layers of the pre-trained deep learning network, the weights from at least one of the layers of the extreme learning machine network to the next layer of the extreme learning machine network are trainable but the weights from at least another one of the layers of the extreme learning machine network to the next layer of the extreme learning machine network are fixed, and the nodes of the output layer of the extreme learning machine network indicate whether a given surface defect class is displayed by a given image; providing a set of pixelated training images of aircraft engine components exhibiting examples of different classes of surface defect, the training images being labelled with the surface defect classes exhibited by their respective components such that each class of surface defect is represented by a respective subset of the training images, and each training image being divided into one or more patches which are respective sub-areas of that image; and training the trainable weights of the extreme learning machine network on the set of training images by inputting each patch into the input layer of the pre-trained deep learning network and adjusting the trainable weights on the basis of a comparison between the node values of the output layer of the extreme learning machine network and the surface defect class label of the parent training image of that patch; whereby the layers of the pre-trained deep learning network from its input layer to its convolutional layer from which the values of the nodes of the input layer of the extreme learning machine network derive, together with the layers of the extreme learning machine network after the training of its trainable weights form a combined network for detecting surface defects in aircraft engine components having an input layer which is the input layer of the pre-trained deep learning network and an output layer which is the output layer of the extreme learning machine network. providing (i) a pre-trained deep learning network having, in sequence, an input layer for receiving pixel values of pixelated images of objects, a set of convolutional layers, a set of fully-connected layers, and an output layer, wherein the pre-trained deep learning network has fixed weights and is pre-trained, and (ii) an extreme learning machine network having, in sequence, an input layer, at least one hidden layer, and an output layer, wherein the values of the nodes of the input layer of the extreme learning machine network derive from the values of the nodes of one of the convolutional layers of the pre-trained deep learning network, the weights from at least one of the layers of the extreme learning machine network to the next layer of the extreme learning machine network are trainable but the weights from at least another one of the layers of the extreme learning machine network to the next layer of the extreme learning machine network are fixed, and the nodes of the output layer of the extreme learning machine network indicate whether a given surface defect class is displayed by a given image; providing a set of pixelated training images of aircraft engine components exhibiting examples of different classes of surface defect, the training images being labelled with the surface defect classes exhibited by their respective components such that each class of surface defect is represented by a respective subset of the training images, and each training image being divided into one or more patches which are respective sub-areas of that image; and training the trainable weights of the extreme learning machine network on the set of training images by inputting each patch into the input layer of the pre-trained deep learning network and adjusting the trainable weights on the basis of a comparison between the node values of the output layer of the extreme learning machine network and the surface defect class label of the parent training image of that patch; whereby the layers of the pre-trained deep learning network from its input layer to its convolutional layer from which the values of the nodes of the input layer of the extreme learning machine network derive, together with the layers of the extreme learning machine network after the training of its trainable weights form a combined network for detecting surface defects in aircraft engine components having an input layer which is the input layer of the pre-trained deep learning network and an output layer which is the output layer of the extreme learning machine network.