We present a method for assigning an ensemble of conformational states to each amino acid residue of a sequence. The states are defined as regions in the (phi, psi) map. The procedure is based on the use of conformational filters. In each filter we use a different set of approximations to estimate the probability of conformational states, and retain only the ones whose probability exceeds an acceptance probability. The resulting state assignment is not necessarily unique, but provides information that can be further exploited in searches for the tertiary structure. This conformational filtering approach to the de novo analysis of a sequence has a number of advantages over traditional structure prediction. First, it is possible to select acceptance probabilities such that the true conformational state is retained for up to 87% of residues, while substantially reducing the number of potential conformations. Second, in solution most linear peptides are present as ensembles of rapidly interconverting conformers, and such ensembles can be well predicted by filtering. Third, we can use Markov chains instead of a statistical mechanical (Ising) treatment, and avoid the need for estimating statistical weight matrices valid for the molecule as a whole. Markov models can use local transition matrices that are assumed to be independent of the rest of the chain, and are directly calculated from pairwise data. We show here that the locally identifiable transition matrices are transferable from the crystal structures of proteins to the solution structures of short peptides, and the ensembles of filtered conformations are in good agreement with nuclear magnetic resonance data. When applied to proteins, the filters retain several conformational states for most residues, and provide a measure of conformational variability. Small variability means that the segment is well defined by local interactions alone, and hence is likely to preserve its structure when isolated from the rest of the chain. Conversely, the structure of a segment with above-average conformational variability is likely to be significantly affected by its protein environment.