We investigate the impact of selector device in cross-point resistive switching memory (RRAM) array on weighted sum operation in the neural network. The requirement of selector devices in a neuromorphic system may be different than that in a conventional memory system. In this work, we developed Verilog-A device models to accurately describe current-voltage (I-V) characteristics of one-selector and one-RRAM (1S-1R) devices obtained experimentally, and then performed the array-level SPICE simulations for weighted sum operation. The weighted sum accuracy is benchmarked as a function of selector non-linearity, array size, and wire resistance. Our results reveal that linearity of the I-V curve in the 1S-1R device with respect to input vector plays an important role in precisely reading-out the weighted sum. Finally, we discuss the desired characteristics of the selector device to be used for the inference stage of the neuromorphic systems.