A 13-itemed scenario framework for classifier single training parameter optimization is developed. Formally, the problem is to find global extremum (mostly, minimum) of function as a classifier output parameter against its single training parameter. Linking the scenario theory to praxis, the classifier type has been decided on two-layer perceptron. Its input objects are monochrome images of a medium format, having a few thousands independent features. Within the framework, the programming environment has been decided on MATLAB, having powerful Neural Network Toolbox. Keeping in mind the stochasticity of the being minimized function, there is defined statistical -stability of its evaluation by a finite set of data. These data are mined in batch testings of the trained classifier. For exemplification of the scenario framework, there is optimized pixel-to-turn standard deviations ratio for training two-layer perceptron in classifying monochrome 60-by-80-images of the enlarged 26 English alphabet capital letters. The goal is to find a pixel-to-turn standard deviations ratio for the training process in order to ensure minimum of classification error percentage. The optimization relative gain is about a third. The developed framework can be applied also for classifier multivariable optimization, wherein it instructs which item operations shall regard the corresponding multiplicity of variables

classifier training parameter optimization, statistical evaluation, optimization scenario, two-layer perceptron, classification error percentage, turned objects classification, monochrome image, pixel-to-turn standard deviations ratio, training set