This paper introduces a method for estimating the maximum depth (sub-millimeter) of minor cracks on the surface of aluminum plates used in the aeronautical industry. A set of C-scan eddy current (EC) images, including real and imaginary parts of the impedance, is analyzed to extract suitable features after reducing noise effects, such as background noises and edge noises. Based on the obtained features, e.g. maximum impedance, the background feature, background noises, type of sensors, a Multilayer Perceptron (MLP) Neural Network is built to estimate the maximum depth of the cracks. The network is optimized based on loss functions, such as mean absolute error and mean squared error. An optimal network structure with five neurons in the first hidden layer and eight neurons in the second hidden layer is chosen. The obtained result indicated that the relative error of estimations is lower than 10% for almost all experimental tested samples.