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In this work neural network models are used to reconstruct in-cylinder pressure from a vibration signal measured from the engine surface by a low-cost accelerometer. Using accelerometers to capture engine combustion is a cost-effective approach due to their low price and flexibility. The paper describes a virtual sensor that re-constructs the in-cylinder pressure and some of its key parameters by using the engine vibration data as input. The vibration and cylinder pressure data have been processed before the neural network model training. Additionally, the correlation between the vibration and in-cylinder pressure data is analyzed to show that the vibration signal is a good input to model the cylinder pressure.The approach is validated on a RON95 single cylinder research engine realizing homogeneous charge compression ignition (HCCI). The experimental matrix covers multiple load/rpm steady-state operating points with different start of injection and lambda setpoints.