Determination of accurate limit of cutting condition in order to obtain broken chips for various chip breaker geometries is essential to improve the machinability. This work presents a hybrid model based on the ratio of broken chip radius to the initial radius of chip to predict the type of chip regarding the characteristics of a chip breaker geometry and cutting parameters. An analytical geometrical model was developed to calculate the initial radius of chip. After running experimental tests for four types of chip breaker geometries and calculation of their chip ratio, type of chips and tool–chip contact were selected as two criteria for classifying chip ratio into three limits representing usable, acceptable, and unacceptable chips. Finally, the normalized data were used to train a neural network model to predict the type of chip which was verified by experiments carried out on a new chip breaker geometry. The trained network could predict the type of chip accurately by providing the geometrical details of the chip breaker and cutting parameters for the network.