Survival analysis with categorical variable I have event time data for subjects with different categories (A, B, C etc.) yearly observed. To my understanding my data is both right and interval censored (?). 
Subjects' category can change from year to year, e.g. A for years 0 to 2, than B from year 3 onwards for subject 1 etc. Each subject can be exposed to the "event" independent of its category. For time being I am not interested in transition effects.
Which estimators can be suggested for the 1 year failure probability per category?  
 A: You can split each of your patients up into multiple records. For example, if Patient Joe is followed for 5 years, switching from A to B two years in, and B to C two years after that, he'd be three records. Joe # 1 who entered at Year 0, and left at year 2. Joe #2 who enters at year 2 and leaves at year 4, and Joe #3, who enters at year 4 and leaves at year 5.
You then use a robust variance estimator that takes care of the fact that you have some non-independence in your data, and you can run any survival analysis you want. I suspect if you're looking for a 1 year probability of failure, you'd use some parametric estimator of the survival curve, or a Kaplan-Meyer type analysis.
A: The simplest approach is to break up each subject into multiple person-years with each year associated with only one category and an event yes/no indicator. You can get yearly probabilities from this without difficulties. Note that this would assume that the probabilities are constant over time. Poisson regression can be used for inference.
A: If I understand you description correctly, you case falls within discrete time survival/hazard analysis (discrete time in the sense of discretized intervals of a continuous process, not in the sense that events only happen after fixed intervals).
In that case I would follow Aniko's suggestion and use a logistic regression model with person-years as observations, with event occurrence as a dependent variable and category as a time-variant explanatory variable (together with time of course).
An applied handbook recommended for this case is Applied Longitudinal Data Analysis by Singer & Willett, ch. 10-12. See here for worked examples/syntax for different programs.
