This paper presents results from experiments on determining the sigmoidal transfer function in Intel's Electronically Trainable Analog Neural Network (ETANN), the 80170NX chip. Accurate simulator training off-chip is needed in order to reduce training time and to minimize chip-in-loop training (on-chip), which if done in excess, can decrease the chip's useful life. For this reason accurate characterization of the ETANN chip is of significant importance to application designers for off-chip simulation. A series of tests were performed to collect data from eight ETANN chips for analysis. After computing an average response value from eight chips and performing a minimum mean-square-error search for a gain coefficient (also called a hardness parameter in the sigmoidal function), a transfer function and gain coefficient were found. Using this transfer function and gain coefficient in a custom neural network simulator, called Neural Graphics, a performance evaluation was accomplished. A test shows a direct correlation between the Neural Graphics simulator output and the ETANN chip's output using the same synaptic weights and the test data. Moreover, for this test we have found that Neural Graphics, while using the characterized transfer function from this research, performed in a superior manner to that of iNNTS simulators. For researchers who desire to interface their own custom simulator with the ETANN hardware, a similar procedure as developed in this paper should be followed.