The relationship between single neuronal activity in area MT and motion perception is a well studied phenomenon. Less is known about how larger fractions of neurons interact to produce a certain perceptual outcome. Here we asked whether pooling the responses of a large population of MT neurons with widely varying properties could improve the predictability of perceptual decisions during ambiguous visual stimulation. Two well trained rhesus monkeys indicated the perceived direction of rotation of bistable structure-from-motion (SFM) stimuli by pushing one of two levers. During this task, multi-channel intracortical recordings including single-unit activity (SUA), multi-unit activity (MUA), and local field potentials (LFP) were collected from area MT. We sorted the neural data according to the monkeys’ behavioral choices and utilized the measure of choice probability (Britten et al., 1996) to quantify the relationship between the different signals and perceptual report. We found that SUA, MUA and LFP all had a rather modest capability of predicting the monkeys’ perceptual report when considered in isolation. We developed optimal predictors for each type of neural signal by selecting the weight for each channel and combining the signals from multiple channels. We found that the combination of simultaneously collected data greatly improved the prediction accuracy of each of the signals. Furthermore, we found that by combining all these three types of neural signals from multiple channels, choice probability increased even further in a systematic way. The accuracy and statistical power of determining the monkeys’ perception increased with the number of cha