An investigation has been made into symmetry features of patterns as a means by which the patterns are described, or with which they are transformed prior to classification in order to assist a pattern recognition system.There are two main points of departure from existing symmetry use in the pattern recognition domain. The first is the adoption of the theory that patterns can be categorised solely using a map of the symmetry features that exist within the static pattern. The second is the application of symmetry transforms to aid non-trivial recognition in patterns which are not intended to be perfectly symmetrical.An experiment is conducted to classify the reflectional symmetry features of digits, using the Generalised Symmetry Transform to produce the features and Probabilistic Neural Networks to perform the classification. Symmetry feature information is also used to define parameters of affine transformations to achieve improved performance in tolerance to variances in position and orientation.The results lead to an investigation of the role of asymmetry. The Generalised Symmetry Transform is modified to produce two related transforms: the Generalised Asymmetry Transform and the Generalised Asymmetry and Symmetry Transform.Finally, a new symmetry transform is proposed which separates the factors affecting the degree of symmetry in an image into three non-linear functions of corresponding pairs of pixels. These factors are: the colour intensity values; the pixel orientations; and the respective distance between the point and potential reflection plane. The strictness of symmetry, or tolerance to non-symmetrical artifacts, is defined in variable parameters which are set to suit the desired application. This new transform is called the Reflectional Symmetry Transform. The structure of its input and output match those of the Generalised Symmetry Transform, which it is intended to replace.