Many industrial inspection tasks can be performed satisfactorily by conventional image processing systems which utilise feature extraction and related feature measuring algorithms. However, for the inspection of parameters such as texture or colour, it also may be necessary to adopt an AI technique which involves the use of artificial neural networks. This paper outlines one such system (WISE) which incorporates algorithms for both conventional image processing used for position and rotation normalisation, and n-tuple processing for subsequent pattern classification. Since its initial implementation, WISE has undergone considerable development and is now capable of inspecting various types of pattern space such as 2-D shape space and 3-D colour space. Results obtained from the use of these pattern spaces are documented as are results for ‘travelling window inspection’, ‘zoned H-S-I inspection’, ‘retinal simulation’, ‘% inspection coverage’, ‘inspection sensitivity’ and ‘archetypal inspection artefacts’. In conclusion, the present highly evolved WISE system provides excellent recognition performance for artefacts which are both complex and coloured. Although, at present, WISE is implemented in software (C+ +) and, therefore, slow in operation, it is considered that, by using special-purpose hardware and/or parallel processing techniques, operational speed approaching TV frame rate will be feasible in the future.