Mass spectral profiling of serum or plasma is one of the tools widely used to make experimental diagnostic systems for different cancer types. In this approach, a set of discriminatory peaks serves as a multiplex cancer biomarker. Hence, adequate selection of peaks is a crucial stage in the development of diagnostic rule. In the present paper we propose using sequential filter and wrapper feature selection in a complete cross-validation scheme with feature selection performed at each run of crossvalidation separately. Filter feature selection is represented by hierarchical cluster analysis; recursive feature elimination coupled with support vector machine is utilized as a wrapper feature selection method. The method performance is demonstrated on previously obtained dataset with ovarian cancer and non-cancer sera. Application of our approach led to a slight but statistically significant increase in accuracy. Peak clustering favoured more stable results of feature selection and provided a biological meaning to selected m/z values. We recommend clustering of peaks as a filter dimensionality reduction for further use in mass spectral studies.