With the emergence of a global economy, companies are more than ever pressured for improved efficiency. Int he transportation industry there is a growing need for better tracking of the status of containers in transit. This would lead to improved handling operation, reduce the number of errors, increase the throughput and enable the use of electronic data interchange (EDI). As electronic tags are not generalized in this industry, containers identification must rely on optical character recognition of the codes printed on the containers. OCR has been one of the first applications envisaged for optical correlation technologies as a result of their high-speed direct detection and identification capabilities. Until now though, most of the work in this area had been performed on computer-generated symbols. Field applications however, must cope with varying symbol fonts and sizes, colors and backgrounds, illumination levels, etc. Environmental variables such as dust, dirt and rust must also be accounted for. Together, these variables lead to a hard-to- solve problem. This paper presents INO's optical correlator and discusses the methods used to generate the identification vectors from which the OCR classification is achieved. It is shown that good results can be obtained on gray-scale real- life images when a multiple composite-filters strategy combined to an innovative classification method.