This paper examines the use of multi-band reconstructed phase spaces as models for phoneme classification. Sub-banding reconstructed phase spaces combines linear, frequency-based techniques with a nonlinear modeling approach to speech recognition. Experiments comparing the effects of filtering speech signals for both reconstructed phase space and traditional speech recognition approaches are presented. These experiments study the use of two non-overlapping subbands for isolated phoneme classification on the TIMIT corpus. It is shown that while classification accuracy using Mel frequency cepstral coefficients as features does not improve with sub-banding, the accuracy increases from 36.1% to 42.0% using sub-banded reconstructed phase spaces to model the phonemes.