Despite recent progress in systems neuroscience, basic properties of the neural code still remain obscure. For instance, the responses of single neurons are both highly variable and ambiguous (similar responses can be elicited by different types of stimuli). This variability/ambiguity has to be resolved by considering the joint pattern of firing of multiple single units responding simultaneously to a stimulus. Therefore, in order to understand the underlying principles of the neural code it is important to characterize the correlations between neurons and the impact that these correlations have on the amount of information that can be encoded by populations of neurons. Here we applied the technique of chronically implanted, multiple tetrodes to record simultaneously from a number of neurons in the primary visual cortex (V1) of the awake behaving macaque, and to measure the correlations in the trial-to-trial fluctuations of their firing rates under the same stimulation conditions (“noise” correlations). We find that, contrary to widespread belief, “noise” correlations in V1 are very small (around 0.01) and do not change systematically neither as a function of cortical distance (up to 600 ?m) nor as a function of the similarity in stimulus preference between the neurons (uniform correlation structure). Interestingly, a uniform correlation structure is predicted by theory to increase the achievable encoding accuracy of a neuronal population and may reflect a universal principle for population