•In-cylinder pressure is reconstructed from engine structure-borne acoustic emission.•A wide range of engine speed, load, and fuel type is considered in this study.•The Hilbert transform of AE is used in the modelling.•The modelling is done based complex cepstrum and neural network. The importance of the in-cylinder pressure transducer has been proven in revealing the information about combustion and exhaust pollution formation, as well as for its capability to classify knock. Due to their high price, they are not used commercially for engine health monitoring, which is of significant importance. Hence, this study will investigate the reconstruction of the in-cylinder pressure trace using a structure-borne acoustic emission (AE) sensor, which are relatively low cost sensors. As shown in the literature, AE indicators show a strong correlation with in-cylinder pressure parameters in both time and crank angle domain. In this study, to avoid the effect of engine speed fluctuations, the reconstruction is done in the crank angle domain by means of the Hilbert transform of AE. Complex cepstrum signal processing analysis with a feed-forward neural network is used to generate a reconstruction regime. Furthermore, the reconstructed signals are used to determine some of the important in-cylinder parameters such as peak pressure (PP), peak pressure timing (PPT), indicated mean effective pressure (IMEP) and pressure rise rate. Results showed that the combination of cepstrum analysis with neural network is capable of reconstructing pressure using AE, regardless of engine load, speed and fuel type. The reconstructed pressure can be used to reliably determine PP and PPT. IMEP can be estimated as well in a reasonable range.