The thesis explores how non-target objects influence object recognition. In all five experiments, sets of non-target objects are used to generate 'scene' contexts and these are presented so that they surround individual target objects. The foci of investigation are (1) whether scene context effects with multiple objects exist, (2) if they exist are they perceptual or due to response biases, (3) what role does the distribution of attention play in the generation of scene context effects, and (4) what is the time-course of their generation? Experiments 1-3 found that target objects were named more accurately when non-target objects were semantically related (context-consistent) than semantically unrelated (context-inconsistent). However the magnitude of the context effect was mediated by visual attention. A significant effect was only achieved when all objects (targets and non-targets) were within an attended region and not when non-targets fell outside of this region. Experiment 4 used a paradigm conceptually related to the Reicher-Wheeler paradigm to provide a measure of response bias. A six-alternative forced-choice response design demonstrated a significant influence of scene context even after the data were corrected for response bias; suggesting a perceptual/representational locus to the scene context effect generated by non-target objects on target objects. Experiments 4 and 5 also manipulated the time-course of the onset of non-target objects relative to target objects. The results showed that at least 52msec was required for the presence of non-target objects to influence recognition of target objects. In other words, the scene context effect for multiple non-target objects requires at least 52msec to accumulate. In summary, scene context effects for multiple non-target objects on target objects directly influence the representational processes of target recognition. Furthermore their magnitude is dependent on the distribution of attention across the visual field and the temporal relationship of non-targets and targets. How these factors influence the modelling of object recognition is also considered.