This paper presents basic principles as well as results obtained with our opportunistic and asynchronous object recognition system. The fundamental idea is to transform a static image into several dataflows of images primitives (e.g line segments, circular arcs, regions). The ranking of the primitives in each flow, that is the delay of each one with respect to the others, is a function of the quantitative relevance of each primitive for the recognition. All flows are then integrated into a common dataflow, thus merging primitives of all types. This common flow is then spatially filtered by attention masks provided by a bottom-up focus of attention mechanism. Doing so, the complexity of the recognition problem is drastically reduced. A top-down, purposive grouping is initiated by relevant (early) primitives in the flow, allowing to better the initial image segmentation. Finally, a dynamic indexing scheme benefits from the asynchronous "arrival" of primitives in the dataflow, by concentrating on the most relevant (early) objects hypotheses. Less relevant hypotheses are delayed in time rather than eliminated, and might be reconsidered after expiration of this delay.