The LHCb’s Electromagentic Calorimeter (ECAL) measures the energy that any particle leaves behind when it travels through its sensors. However, with the current granularity, it is not possible to exploit the shape of the shower produced by the particle when it interacts with the ECAL, which is an information that could be enough to conclude what particle is being detected. In an attempt to find out whether it would be possible to classify them in future runs of the LHC, simulated data is generated with Geant4, giving an idea of what SPACAL, an updated version of the current calorimeter with better resolution, is capable of. Convolutional Neural Networks are applied so that the algorithm can understand the shapes and energy deposits produced by each kind of particle. Results obtained demonstrate that bigger resolution in ECAL allows over 95% precision in some classifications such as photons against neutrons.