The objective of this paper was to predict the residual strength of post impacted carbon/epoxy composite laminates using an online acoustic emission (AE) monitoring and artificial neural networks (ANN). The laminates were made from eight-layered carbon (in woven mat form) with epoxy as the binding medium by hand lay-up technique and cured at a pressure of 100 kg/cm2 under room temperature using a 30 ton capacity compression molding machine for 24 h. 21 tensile specimens (ASTM D3039 standard) were cut from the cross ply laminates. 16 specimens were subjected to impact load from three different heights using a Fractovis Plus drop impact tester. Both impacted and non-impacted specimens were subjected to uniaxial tension under the acoustic emission monitoring using a 100 kN FIE servo hydraulic universal testing machine. The dominant AE parameters such as counts, energy, duration, rise time and amplitude are recorded during monitoring. Cumulative counts corresponding to the amplitude ranges obtained during the tensile testing are used to train the network. This network can be used to predict the failure load of a similar specimen subjected to uniaxial tension under acoustic emission monitoring for certain percentage of the average failure load. The objective of this paper was to predict the residual strength of post impacted carbon / epoxy composite laminates using an online acoustic emission (AE) monitoring and artificial neural networks (ANN). The laminates were made from eight-layered carbon (in woven mat form ) with epoxy as the binding medium by hand lay-up technique and cured at a pressure of 100 kg / cm2 under room temperature using a 30 ton capacity compression molding machine for 24 h. 21 tensile specimens (ASTM D3039 standard) were cut from the Both pectinate and non-impacted specimens were subjected to uniaxial tension under the acoustic emission monitoring using a 100 kN FIE servo hydraulic universal testing machine . The dominant AE parameters such as counts, energy, duration, rise time and amplitude are recorded during monitoring. Cumulative counts corresponding to the amplitude ranges obtained during the tensile testing are used to train the network. This network can be used to predict the failure load of a similar specimen subjected to uniaxial tension under acoustic emission monitoring for certain percentage of the average failure load.