In the High Velocity Oxygen Fuel (HVOF) technology, the coating properties are sensitive to the behaviors of in-flight particles, which are mainly influenced by the processing parameters. However, due to the complex chemical and thermodynamic reactions, the real-time optimization of the coating properties during the HVOF process is still a challenging issue. This study focused on establishing an Artificial Neural Networks (ANN) model to analyze the influence of the processing parameters on the characteristics of in-flight particles. Hydroxyapatite (HA) powders were selected to deposit onto the stainless steel substrates via an improved HVOF spraying system. Combined with an Accuraspray-g3 system applied to acquire the temperature and velocity of inflight HA particles, the artificial neural network algorithm was well trained to predict the velocity and temperature of in-flight particles. The relationship between the variations of the operating parameters (gas flow rates and fuel-to-oxygen ratio) and the behaviors of in-flight HA particles was investigated, which therefor contributes to analyzing and optimizing the mechanical performance and crystallinity of the HA coatings.