The use of context dependent modeling units in hand- writing recognition has been considered by many authors as promising substantial performance improvements in sys- tems based on Hidden-Markov models. Interestingly, in the literature only a few approaches limited to online recogni- tion are documented to make use of this technology. There- fore, we investigated whether context dependent model- ing also offers advantages for offline recognition systems. The moderate performance improvements we achieved on a challenging unconstrained handwriting recognition task suggest that context dependent modeling can not easily be exploited for offline recognition. In this paper we will present the principles behind context dependent modeling and discuss the reasons for its limited applicability in rec- ognizing offline handwriting data.