The objective of this research paper is to predict the ultimate strength of the glass/epoxy composite laminates using the online Acoustic Emission (AE) monitoring and Artificial Neural Networks (ANN). The laminates were made by six layered glass fiber (in woven mat form) with epoxy as the binding medium by hand lay-up technique, cured at room temperature 2 at the pressure of 30 kg/cm . Tensile specimens with ASTM D3039 standard dimensions were cut from the laminates. These specimens were subjected to uni-axial tension under the acoustic emission monitoring using 10 ton capacity universal tensile machine. The dominant AE parameters such as counts, energy, duration, RMS, ASL ,and amplitude are recorded during monitoring. The RMS value corresponding to the amplitude ranges obtained during tensile testing were used to predict the failure load of a similar specimen subjected to uni-axial tension well before its failure load.