This paper proposes a novel gradient descent least squares regression (GDLSR)-based neural network (NN) structure for the control of grid-integrated solar photovoltaic (PV) system with improved power quality. Here, a single-layer neuron structure is used for the extraction of fundamental component (FC) from the load current. During FC extraction, GDLSR-based NN structure attenuates harmonic components, noise, dc offsets, bias, notches and distortions from the nonlinear current, which improves the power quality under normal as well as under abnormal grid conditions. This single-layer GDLSR-based NN structure has a very simple architecture, which decreases the computational burden and algorithm complexity. Therefore, it is easy in implementation. In this work, the GDLSR-based control technique is tested on a single-phase single-stage grid-integrated PV topology with the nonlinear loads. The prime objective of the GDLSR-based NN structure is to provide reactive power compensation, power factor correction, harmonics filtering and mitigation of other power quality issues. Moreover, when solar irradiation is zero, the dc-link capacitor and voltage-source converter act as a distribution static compensator, which enhances the utilization factor of the system. The proposed system is modeled, and its performances are verified experimentally on a developed prototype in different grid disturbances as well as solar insolation variation conditions, which performances have satisfied the motive of the proposed technique as well as the IEEE-519 standard. © 1982-2012 IEEE.