The article is devoted to binary video signal multi-parameter classifiers for object deformation measurement. The study examines an applied task. It is the rail deformation estimation of a railway track, which consists of several parts with different rail types. The problem is related to ensuring traffic safety on the railway. If the rail type is incorrectly recognized, the deformation parameters will be measured with a high error, which can lead to an incorrect assessment of the track condition and the omission of a potentially dangerous situation. The rail shape is measured using a machine vision system installed on a track measuring car. The signal of this system, representing a binary video signal(contour), is input to the classifier. Then the signal and the result of rail type recognition are used to estimate the deformation. The classifier meets the requirements to ensure real-time processing and high quality recognition. Therefore, the classifier takes into account the properties of the processed signals, on the one hand, simplifies processing, and on the other hand, ensures a low probability of classification error. One of the features of the classifier is its parametricity. The matching parameters were introduced into the classifier model: offset along the abscissa and ordinary axis, rotation angle. Parametricity allows to reduce the likelihood of recognition error. The classifier can be expanded to any number of types of rails, which makes it a universal solution for various roads, both Russian and European. The analysis of the measured deformation parameters showed that the proposed multi-parameter classifier provides a high accuracy (0.1 mm in the "wear side ' parameter as the maximum absolute difference for the 0.95 level quantile). The proposed classifier can be used to estimate the condition of railway tracks.