Additive manufacturing, also known as 3D printing, is a new technology that obliterates the geomet-rical limits of the produced workpieces and promises low running costs as compared to traditionalmanufacturing methods. Hence, additive manufacturing technology has high expectations in industry.Unfortunately, the lack of a proper quality monitoring prohibits the penetration of this technology intoan extensive practice. This work investigates the feasibility of using acoustic emission for quality mon-itoring and combines a sensitive acoustic emission sensor with machine learning. The acoustic signalswere recorded using a fiber Bragg grating sensor during the powder bed additive manufacturing processin a commercially available selective laser melting machine. The process parameters were intentionallytuned to invoke different processing regimes that lead to the formation of different types and concen-trations of pores (1.42 ± 0.85 %, 0.3 ± 0.18 % and 0.07 ± 0.02 %) inside the workpiece. According to thispoor, medium and high part qualities were defined. The acoustic signals collected during processing weregrouped accordingly and divided into two separate datasets; one for the training and one for the testing.The acoustic features were the relative energies of the narrow frequency bands of the wavelet packettransform, extracted from all the signals. The classifier, based on spectral convolutional neural network,was trained to differentiate the acoustic features of dissimilar quality. The confidence in classificationsvaries between 83 and 89 %. In view of the narrow range of porosity, the results can be considered aspromising and they showed the feasibility of the quality monitoring using acoustic emission with thesub-layer spatial resolution.